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Invited Public Lecture Room 204 Runme Shaw Building HKU
Faculty of Education The University of Hong Kong 17 November 2016
Teaching and Learning Analytics for the Classroom Teacher
Professor Demetrios G Sampson
PhD(ElectEng) (Essex) PgDip (Essex) BEngMEng(Elec) (DUTH) CEng Golden Core Member IEEE Computer Society
Editor-In-Chief Educational Technology amp Society Journal Chair IEEE Technical Committee on Learning Technologies
Professor Learning Technologies | School of Education Curtin University Australia
Presentation Overview
Introduction Educational Data for supporting Data-Driven Decision
Making in School Education Teaching Analytics Analyse your Lesson Plans to
Improve them Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students Teaching and Learning Analytics to Support Teacher
Inquiry
Introduction
Perth Western Australia
Perth Western Australia
Curtin University
Curtin University
School of Education Offers programs that embrace innovation in education theory and practice since 1975 with the aim of preparing highly competent
graduates who can teach and work in a fast-changing world
The main provider of Teacher Education in Western Australia 45 WA school graduates 1000 new UG students annually
The dominant online provider of Teacher Education in Australia with over 2000 students through Open Universities Australia
Recognised within Top 100 Worldwide in the subject of Education
by QS World University Rankings by Subject 201516
Joined School of Education Curtin University October 2015
20 years in Learning Technologies and Technology Enhanced Learning
bull 17 years in Academia and Research School of Education Curtin University Western Australia Dept of Digital Systems University of Piraeus Greece Information Technologies Institute Centre of Research and Technology - Hellas Greece (since January 2000)
bull 3 in Industry Research amp Innovation DirectorConsultant in Educational Technology industry and Greek Ministry of Education (September 1996 ndash December 1999)
bull PhD in Electronic Systems Engineering University of Essex UK (1995) bull Diploma in Electrical Engineering Democritus University of Thrace Greece (1989)
bull 67 Research amp Innovation projects with external funding at the range of 15 Millioneuro bull 390 research publications in scientific books journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40 during the past 5 years] bull 9 times Best Paper Award in International Conferences in LT and TeL bull KeynoteInvited Speaker in 72 InternationalNational Conferences [60 during the past 5 years] bull Supervised 150 honours and postgraduate students to successful completion
bull Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011 2016-today) bull Editor-in-Chief of the Educational Technology and Society Journal (listed 4 in Scholar Google Top
publications of Educational Technology (httpsgooglkHa6vk) bull Founding Board Member Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed 11 in the same Scholar Google list)
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
Presentation Overview
Introduction Educational Data for supporting Data-Driven Decision
Making in School Education Teaching Analytics Analyse your Lesson Plans to
Improve them Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students Teaching and Learning Analytics to Support Teacher
Inquiry
Introduction
Perth Western Australia
Perth Western Australia
Curtin University
Curtin University
School of Education Offers programs that embrace innovation in education theory and practice since 1975 with the aim of preparing highly competent
graduates who can teach and work in a fast-changing world
The main provider of Teacher Education in Western Australia 45 WA school graduates 1000 new UG students annually
The dominant online provider of Teacher Education in Australia with over 2000 students through Open Universities Australia
Recognised within Top 100 Worldwide in the subject of Education
by QS World University Rankings by Subject 201516
Joined School of Education Curtin University October 2015
20 years in Learning Technologies and Technology Enhanced Learning
bull 17 years in Academia and Research School of Education Curtin University Western Australia Dept of Digital Systems University of Piraeus Greece Information Technologies Institute Centre of Research and Technology - Hellas Greece (since January 2000)
bull 3 in Industry Research amp Innovation DirectorConsultant in Educational Technology industry and Greek Ministry of Education (September 1996 ndash December 1999)
bull PhD in Electronic Systems Engineering University of Essex UK (1995) bull Diploma in Electrical Engineering Democritus University of Thrace Greece (1989)
bull 67 Research amp Innovation projects with external funding at the range of 15 Millioneuro bull 390 research publications in scientific books journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40 during the past 5 years] bull 9 times Best Paper Award in International Conferences in LT and TeL bull KeynoteInvited Speaker in 72 InternationalNational Conferences [60 during the past 5 years] bull Supervised 150 honours and postgraduate students to successful completion
bull Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011 2016-today) bull Editor-in-Chief of the Educational Technology and Society Journal (listed 4 in Scholar Google Top
publications of Educational Technology (httpsgooglkHa6vk) bull Founding Board Member Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed 11 in the same Scholar Google list)
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
Introduction
Perth Western Australia
Perth Western Australia
Curtin University
Curtin University
School of Education Offers programs that embrace innovation in education theory and practice since 1975 with the aim of preparing highly competent
graduates who can teach and work in a fast-changing world
The main provider of Teacher Education in Western Australia 45 WA school graduates 1000 new UG students annually
The dominant online provider of Teacher Education in Australia with over 2000 students through Open Universities Australia
Recognised within Top 100 Worldwide in the subject of Education
by QS World University Rankings by Subject 201516
Joined School of Education Curtin University October 2015
20 years in Learning Technologies and Technology Enhanced Learning
bull 17 years in Academia and Research School of Education Curtin University Western Australia Dept of Digital Systems University of Piraeus Greece Information Technologies Institute Centre of Research and Technology - Hellas Greece (since January 2000)
bull 3 in Industry Research amp Innovation DirectorConsultant in Educational Technology industry and Greek Ministry of Education (September 1996 ndash December 1999)
bull PhD in Electronic Systems Engineering University of Essex UK (1995) bull Diploma in Electrical Engineering Democritus University of Thrace Greece (1989)
bull 67 Research amp Innovation projects with external funding at the range of 15 Millioneuro bull 390 research publications in scientific books journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40 during the past 5 years] bull 9 times Best Paper Award in International Conferences in LT and TeL bull KeynoteInvited Speaker in 72 InternationalNational Conferences [60 during the past 5 years] bull Supervised 150 honours and postgraduate students to successful completion
bull Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011 2016-today) bull Editor-in-Chief of the Educational Technology and Society Journal (listed 4 in Scholar Google Top
publications of Educational Technology (httpsgooglkHa6vk) bull Founding Board Member Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed 11 in the same Scholar Google list)
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
Perth Western Australia
Perth Western Australia
Curtin University
Curtin University
School of Education Offers programs that embrace innovation in education theory and practice since 1975 with the aim of preparing highly competent
graduates who can teach and work in a fast-changing world
The main provider of Teacher Education in Western Australia 45 WA school graduates 1000 new UG students annually
The dominant online provider of Teacher Education in Australia with over 2000 students through Open Universities Australia
Recognised within Top 100 Worldwide in the subject of Education
by QS World University Rankings by Subject 201516
Joined School of Education Curtin University October 2015
20 years in Learning Technologies and Technology Enhanced Learning
bull 17 years in Academia and Research School of Education Curtin University Western Australia Dept of Digital Systems University of Piraeus Greece Information Technologies Institute Centre of Research and Technology - Hellas Greece (since January 2000)
bull 3 in Industry Research amp Innovation DirectorConsultant in Educational Technology industry and Greek Ministry of Education (September 1996 ndash December 1999)
bull PhD in Electronic Systems Engineering University of Essex UK (1995) bull Diploma in Electrical Engineering Democritus University of Thrace Greece (1989)
bull 67 Research amp Innovation projects with external funding at the range of 15 Millioneuro bull 390 research publications in scientific books journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40 during the past 5 years] bull 9 times Best Paper Award in International Conferences in LT and TeL bull KeynoteInvited Speaker in 72 InternationalNational Conferences [60 during the past 5 years] bull Supervised 150 honours and postgraduate students to successful completion
bull Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011 2016-today) bull Editor-in-Chief of the Educational Technology and Society Journal (listed 4 in Scholar Google Top
publications of Educational Technology (httpsgooglkHa6vk) bull Founding Board Member Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed 11 in the same Scholar Google list)
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
Perth Western Australia
Curtin University
Curtin University
School of Education Offers programs that embrace innovation in education theory and practice since 1975 with the aim of preparing highly competent
graduates who can teach and work in a fast-changing world
The main provider of Teacher Education in Western Australia 45 WA school graduates 1000 new UG students annually
The dominant online provider of Teacher Education in Australia with over 2000 students through Open Universities Australia
Recognised within Top 100 Worldwide in the subject of Education
by QS World University Rankings by Subject 201516
Joined School of Education Curtin University October 2015
20 years in Learning Technologies and Technology Enhanced Learning
bull 17 years in Academia and Research School of Education Curtin University Western Australia Dept of Digital Systems University of Piraeus Greece Information Technologies Institute Centre of Research and Technology - Hellas Greece (since January 2000)
bull 3 in Industry Research amp Innovation DirectorConsultant in Educational Technology industry and Greek Ministry of Education (September 1996 ndash December 1999)
bull PhD in Electronic Systems Engineering University of Essex UK (1995) bull Diploma in Electrical Engineering Democritus University of Thrace Greece (1989)
bull 67 Research amp Innovation projects with external funding at the range of 15 Millioneuro bull 390 research publications in scientific books journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40 during the past 5 years] bull 9 times Best Paper Award in International Conferences in LT and TeL bull KeynoteInvited Speaker in 72 InternationalNational Conferences [60 during the past 5 years] bull Supervised 150 honours and postgraduate students to successful completion
bull Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011 2016-today) bull Editor-in-Chief of the Educational Technology and Society Journal (listed 4 in Scholar Google Top
publications of Educational Technology (httpsgooglkHa6vk) bull Founding Board Member Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed 11 in the same Scholar Google list)
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
Curtin University
Curtin University
School of Education Offers programs that embrace innovation in education theory and practice since 1975 with the aim of preparing highly competent
graduates who can teach and work in a fast-changing world
The main provider of Teacher Education in Western Australia 45 WA school graduates 1000 new UG students annually
The dominant online provider of Teacher Education in Australia with over 2000 students through Open Universities Australia
Recognised within Top 100 Worldwide in the subject of Education
by QS World University Rankings by Subject 201516
Joined School of Education Curtin University October 2015
20 years in Learning Technologies and Technology Enhanced Learning
bull 17 years in Academia and Research School of Education Curtin University Western Australia Dept of Digital Systems University of Piraeus Greece Information Technologies Institute Centre of Research and Technology - Hellas Greece (since January 2000)
bull 3 in Industry Research amp Innovation DirectorConsultant in Educational Technology industry and Greek Ministry of Education (September 1996 ndash December 1999)
bull PhD in Electronic Systems Engineering University of Essex UK (1995) bull Diploma in Electrical Engineering Democritus University of Thrace Greece (1989)
bull 67 Research amp Innovation projects with external funding at the range of 15 Millioneuro bull 390 research publications in scientific books journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40 during the past 5 years] bull 9 times Best Paper Award in International Conferences in LT and TeL bull KeynoteInvited Speaker in 72 InternationalNational Conferences [60 during the past 5 years] bull Supervised 150 honours and postgraduate students to successful completion
bull Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011 2016-today) bull Editor-in-Chief of the Educational Technology and Society Journal (listed 4 in Scholar Google Top
publications of Educational Technology (httpsgooglkHa6vk) bull Founding Board Member Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed 11 in the same Scholar Google list)
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
Curtin University
School of Education Offers programs that embrace innovation in education theory and practice since 1975 with the aim of preparing highly competent
graduates who can teach and work in a fast-changing world
The main provider of Teacher Education in Western Australia 45 WA school graduates 1000 new UG students annually
The dominant online provider of Teacher Education in Australia with over 2000 students through Open Universities Australia
Recognised within Top 100 Worldwide in the subject of Education
by QS World University Rankings by Subject 201516
Joined School of Education Curtin University October 2015
20 years in Learning Technologies and Technology Enhanced Learning
bull 17 years in Academia and Research School of Education Curtin University Western Australia Dept of Digital Systems University of Piraeus Greece Information Technologies Institute Centre of Research and Technology - Hellas Greece (since January 2000)
bull 3 in Industry Research amp Innovation DirectorConsultant in Educational Technology industry and Greek Ministry of Education (September 1996 ndash December 1999)
bull PhD in Electronic Systems Engineering University of Essex UK (1995) bull Diploma in Electrical Engineering Democritus University of Thrace Greece (1989)
bull 67 Research amp Innovation projects with external funding at the range of 15 Millioneuro bull 390 research publications in scientific books journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40 during the past 5 years] bull 9 times Best Paper Award in International Conferences in LT and TeL bull KeynoteInvited Speaker in 72 InternationalNational Conferences [60 during the past 5 years] bull Supervised 150 honours and postgraduate students to successful completion
bull Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011 2016-today) bull Editor-in-Chief of the Educational Technology and Society Journal (listed 4 in Scholar Google Top
publications of Educational Technology (httpsgooglkHa6vk) bull Founding Board Member Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed 11 in the same Scholar Google list)
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
School of Education Offers programs that embrace innovation in education theory and practice since 1975 with the aim of preparing highly competent
graduates who can teach and work in a fast-changing world
The main provider of Teacher Education in Western Australia 45 WA school graduates 1000 new UG students annually
The dominant online provider of Teacher Education in Australia with over 2000 students through Open Universities Australia
Recognised within Top 100 Worldwide in the subject of Education
by QS World University Rankings by Subject 201516
Joined School of Education Curtin University October 2015
20 years in Learning Technologies and Technology Enhanced Learning
bull 17 years in Academia and Research School of Education Curtin University Western Australia Dept of Digital Systems University of Piraeus Greece Information Technologies Institute Centre of Research and Technology - Hellas Greece (since January 2000)
bull 3 in Industry Research amp Innovation DirectorConsultant in Educational Technology industry and Greek Ministry of Education (September 1996 ndash December 1999)
bull PhD in Electronic Systems Engineering University of Essex UK (1995) bull Diploma in Electrical Engineering Democritus University of Thrace Greece (1989)
bull 67 Research amp Innovation projects with external funding at the range of 15 Millioneuro bull 390 research publications in scientific books journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40 during the past 5 years] bull 9 times Best Paper Award in International Conferences in LT and TeL bull KeynoteInvited Speaker in 72 InternationalNational Conferences [60 during the past 5 years] bull Supervised 150 honours and postgraduate students to successful completion
bull Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011 2016-today) bull Editor-in-Chief of the Educational Technology and Society Journal (listed 4 in Scholar Google Top
publications of Educational Technology (httpsgooglkHa6vk) bull Founding Board Member Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed 11 in the same Scholar Google list)
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
Joined School of Education Curtin University October 2015
20 years in Learning Technologies and Technology Enhanced Learning
bull 17 years in Academia and Research School of Education Curtin University Western Australia Dept of Digital Systems University of Piraeus Greece Information Technologies Institute Centre of Research and Technology - Hellas Greece (since January 2000)
bull 3 in Industry Research amp Innovation DirectorConsultant in Educational Technology industry and Greek Ministry of Education (September 1996 ndash December 1999)
bull PhD in Electronic Systems Engineering University of Essex UK (1995) bull Diploma in Electrical Engineering Democritus University of Thrace Greece (1989)
bull 67 Research amp Innovation projects with external funding at the range of 15 Millioneuro bull 390 research publications in scientific books journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40 during the past 5 years] bull 9 times Best Paper Award in International Conferences in LT and TeL bull KeynoteInvited Speaker in 72 InternationalNational Conferences [60 during the past 5 years] bull Supervised 150 honours and postgraduate students to successful completion
bull Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011 2016-today) bull Editor-in-Chief of the Educational Technology and Society Journal (listed 4 in Scholar Google Top
publications of Educational Technology (httpsgooglkHa6vk) bull Founding Board Member Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed 11 in the same Scholar Google list)
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
20 years in Learning Technologies and Technology Enhanced Learning
bull 17 years in Academia and Research School of Education Curtin University Western Australia Dept of Digital Systems University of Piraeus Greece Information Technologies Institute Centre of Research and Technology - Hellas Greece (since January 2000)
bull 3 in Industry Research amp Innovation DirectorConsultant in Educational Technology industry and Greek Ministry of Education (September 1996 ndash December 1999)
bull PhD in Electronic Systems Engineering University of Essex UK (1995) bull Diploma in Electrical Engineering Democritus University of Thrace Greece (1989)
bull 67 Research amp Innovation projects with external funding at the range of 15 Millioneuro bull 390 research publications in scientific books journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40 during the past 5 years] bull 9 times Best Paper Award in International Conferences in LT and TeL bull KeynoteInvited Speaker in 72 InternationalNational Conferences [60 during the past 5 years] bull Supervised 150 honours and postgraduate students to successful completion
bull Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011 2016-today) bull Editor-in-Chief of the Educational Technology and Society Journal (listed 4 in Scholar Google Top
publications of Educational Technology (httpsgooglkHa6vk) bull Founding Board Member Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed 11 in the same Scholar Google list)
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 2367
EDU1x Analytics for the Classroom Teacher
edX MOOC EDU1x Analytics for the Classroom Teacher
Curtin University October-December 2016
More than 2500 enrollments from over 127 countries
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 2467
Educational Data Analytics Technologies for
Data-driven Decision Making in Schools
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 2567
School Autonomy bull School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and more efficient school operations
bull Schools are allowed more freedom in terms of decision making ndash For example curriculum design and delivery human resources management and
infrastructure maintenance and procurement
bull However increased school autonomy introduces the need for robust
evidence of ndash Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards ndash Engaging in continuous School Self-Evaluation and Improvement
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 2667
What is Data-driven Decision Making Data-driven Decision Making (DDDM) in schools is defined as[1]
ldquothe systematic collection analysis examination and interpretation of
data to inform practice and policy in educational settingsrdquo
The aim of data-driven decision making is to report evaluate and improve the processes and outcomes of schools
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 2767
What are Educational Data (12)
bull Educational data can be broadly defined as[2]
ldquoInformation that is collected and organised to represent some aspect of schools This can include any relevant information about students parents schools and teachers derived from
qualitative and quantitative methods of analysisrdquo
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 2867
What is Educational Data (22)
bull Educational Data are generated by various sources both internal and external to the school for example[2] bull Student data
ndash such as demographics and prior academic performance
bull Teacher data ndash such as competences and professional experience
bull Data generated during the teaching learning and assessment processes ndash both within and beyond the physical classroom premises such as lesson plans
methods of assessments classroom management
bull Human Resources Infrastructure and Financial Plan ndash such as educational and non-educational personnel hardwaresoftware expenditure
bull Studentsrsquo Wellbeing Social and Emotional Development ndash such as support respect to diversity and special needs
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 2967
Video How data helps teachers
Data Quality Campaign ‒ Non-profit US organisation to promote the use of
educational data in school education
Outline How a teacher can use educational data to
improve teaching practice [151]
httpswwwyoutubecomwatchv=cgrfiPvwDBw
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 3067
Data Literacy for Teachers (14) Data Literacy for teachers is a core competence defined as[3]
ldquothe ability to understand and use data effectively to inform decisionsrdquo
bull It comprises a competence set (knowledge skills and attitudes)
required to locate collect analyzeunderstand interpret and act upon Educational Data from different sources so as to support improvement of the teaching learning and assessment process[4]
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 3167
Data Literacy for Teachers (24)
Data Literacy for Teachers
Find and collect relevant
educational data [Data Location]
Understand what the educational data represent
[Data Comprehension]
Understand what the
educational data mean
[Data Interpretation]
Define instructional approaches to
address problems identified by the educational data [Instructional
Decision Making]
Define questions on how to
improve practice using the
educational data [Question
Posing]
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 3267
Data Literacy for Teachers (34) bull Data Literacy for teachers is increasingly considered to be a core
competence in
ndash Teachersrsquo pre-service education and licensure standards For example the CAEP Accreditation Standards issued by the Council of Accreditation of Educator Preparation in USA
ndash Teachersrsquo continuing professional development standards For example the InTASC Model Core Teaching Standards issued by the Council of Chief State School Officers in USA
bull Overall data literacy for teachers involves the holistic ability beyond
simple student assessment interpretation (assessment literacy) to meet both continuous school self-evaluation and improvement needs as well as external accountability and compliance to regulatory standards
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 3367
Data Literacy for Teachers (44) bull Despite its importance Data Literacy for Teachers is still not widely
cultivated and additionally a number of barriers can limit the capacity of teachers to use data to inform their practice[5]
Access to educational data bull Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data bull Delayed or late access to data andor their analysis
Quality of educational data bull Verification of the validity of collected data - do they accurately measure what
they are supposed to bull Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support bull A very time- and resource-consuming process (infrastructure and human
resources)
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 3467
Data Analytics technologies (12)
Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources which aim to support and improve decision-making
Data analytics are mature technologies currently applied in real-life financial business and health systems
However they have only recently been considered in the context of Higher Education[6] and even more recently in School Education[7]
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 3567
Data Analytics technologies (22)
bull Educational data analytics technologies to support teaching and learning can be classified into three main types
bull Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery bull The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners bull It can be combined with insights from their implementation using Learning
Analytics
Teaching Analytics
bull Refers to methods and tools for ldquothe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occursrdquo[8]
bull The aim is to improve the learning conditions for learners bull It can be related to Teaching Analytics which analyses the learning context
Learning Analytics
bull Combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry facilitating teachers to reflect on their teaching design using evidence from the delivery to the students
Teaching and Learning Analytics
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 3667
Teaching Analytics Analyse your Lesson Plans
to Improve them
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 3767
Lesson Plans Lesson Plans are[9]
ldquoconcise working documents which outline the teaching and learning that will be conducted within a lessonrdquo
Lesson plans are commonly used by teachers to ‒ Document their teaching designs to help them orchestrate its delivery ‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
Lesson plans are usually structured based on templates which define
a set of elements[10] eg ndash the educational objectivesstandards to be attained by students ndash the flow and timeframe of the learning and assessment activities to be
delivered during the lesson and ndash the educational resources andor tools that will support the delivery of the
learning and assessment activities
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 3867
Teaching Analytics Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective to support self-reflection and analysis for improvement
Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a whole or on individual elements) aiming to improve the learning conditions for their students
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 3967
Teaching Analytics Why do it bull Teaching Analytics can be used to support teaching planning as
follows Analyze classroom teaching design for self-reflection and improvement
bull Visualize the elements of the lesson plan bull Visualize the alignment of the lesson plan to educational objectives standards bull Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to receive feedback
bull Support the process of sharing a lesson plan with peers or mentors allowing them to provide feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with peers
bull Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-reflection
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 4067
Indicative examples of Teaching Analytics as part of Lesson Planning Tools
Venture Logo Tool Venture Teaching Analytics
1 Learning Designer London Knowledge Lab
bull Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of each type of learning and assessment activities
2 MyLessonPlanner Teach With a Purpose LLC
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates a visual report on which educational objective standards are adopted
bull Highlights specific standards that have not been accommodated
3 Lesson Plan Creator StandOut Teaching
bull Validates whether a lesson plan has potential inconsistencies in its design
Generates different types of suggestions for alleviating design inconsistencies (eg time misallocations)
4 Lesson Planner tool OnCourse Systems for Education LLC
bull Analyze classroom teaching design through sharing with peers or mentors to receive feedback
5 Common Curriculum Common Curriculum
bull Analyze classroom teaching design through co-designing and co-reflecting with peers
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 4167
Indicative examples of Teaching Analytics as part of Learning Management Systems
Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle bull Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in Moodle
2 Course Coverage
Reports Blackboard
bull Visualize the alignment of the lesson plan to educational objectives standards
bull Generates an outline of all assessment activities included in the lesson plan
bull Visualises whether they have been mapped to the educational objectives of the lesson
3 Review Course
Design BrightSpace
bull Visualize the alignment of the lesson plan to educational objectives standards
Visualizes how the learning and assessment activities are mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
bull Validate whether a lesson plan has potential inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a specific checklist embedded in the tool
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 4267
Learning Analytics Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your Students
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 4367
Personalized Learning in 21st century school education
bull Personalised Learning is highlighted as a key global priority due to empirical evidence revealing the benefits it can deliver to students
Who Bill and Melinda Gates Foundation and RAND Corporation What Large-scale study in USA to investigate the potential of personalised learning in school education Results Initial results from over 20 schools claim an almost universal improvement in student performance
Who Education Elements What Study with 117 schools from 23 districts in the USA to identify the impact of personalisation on students learning Results Consistent improvement in studentsrsquo learning outcomes and engagement
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 4467
Student Profiles for supporting Personalized Learning (12)
A key element for successful personalised learning is the measurement collection and analysis and report on appropriate student data typically using student profiles
A student profile is a set of attributes and their values that describe a student
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 4567
Student Profiles for supporting Personalized Learning (22)
Types of student data commonly used by schools to build and populate student profiles[11]
Static Student Data Dynamic Student Data
Personal and academic attributes of students Studentsrsquo activities during the learning process
Remain unchanged for large periods of time Generated in a more frequent rate
Usually stored in Student Information Systems Usually collected by the classroom teachers andor Learning Management Systems
Mainly related to bull Student demographics such as age special
education needs bull Past academic performance data such as
history of course enrolments or academic transcripts
They are mainly related to bull Student engagement in the learning
activities such as level of participation in the learning activities level of motivation
bull Student behaviour during the learning activities such as disciplinary incidents or absenteeism rates
bull Student performance such as formative and summative assessment scores
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 4667
Learning Analytics Learning Analytics have been defined as[8]
ldquoThe measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occursrdquo
Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning conditions for individual learners or groups of learners
Therefore Learning Analytics can support ‒ Collection of student data during the
delivery of a teaching design ‒ Analysis and report on student data
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 4767
Learning Analytics Collection of student data
bull Collection of student data during the delivery of a teaching design (eg a lesson plan) aims to buildupdate individual student profiles
bull Types of student data typically collected are ldquoDynamic Student Datardquo ndash Engagement in learning activities For example the progress each student is
making in completing learning activities ndash Performance in assessment activities For example formative or summative
assessment scores ndash Interaction with Digital Educational Resources and Tools for example which
educational resources each student is viewingusing ndash Behavioural data for example behavioural incidents
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 4867
Learning Analytics Analysis and report on student data
Analysis and report on student data aims to provide insights from the learning process and help the teacher to provide personalised interventions
Learning Analytics can provide different types of outcomes utilising both ldquoDynamic Student Datardquo and ldquoStatic Student Datardquo Discover patterns within student data Predict future trends in studentsrsquo progress Recommend teaching and learning actions to either the teacher or the
student
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 4967
Learning Analytics Strands bull Learning Analytics are commonly classified in[12]
Descriptive Learning Analytics bull Depicts meaningful patterns or insights from the analysis of student
data to elicit ldquoWhat has already happenedrdquo bull Related to ldquoDiscover Patterns within student datardquo outcome
Predictive Learning Analytics bull Predicts future trends in student progress to elicit ldquoWhat will
happenrdquo bull Related to ldquoPredict Future Trends in studentsrsquo progressrdquo outcome
Prescriptive Learning Analytics bull Generates recommendations for further teaching and learning
actions supporting ldquoWhat should we dordquo bull Related to ldquoRecommend Teaching and Learning Actionsrdquo outcome
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 5067
Indicative Descriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite bull Engagement in learning activities
bull Interaction with Digital Educational Resources and Tools
Generates reports that outline the performance trends of each student in collaborative project development
2 SmartKlass KlassData
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates dashboards on studentsrsquo individual and collaborative performance in learning and assessment activities
3 Learning Analytics Enhanced Rubric Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grades for each student based on customizable teacher-defined criteria of performance and engagement
4 LevelUp Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Behavioural data
Generates grade points and rankings for each student based on customizable teacher-defined criteria of performance and engagement
5 Forum Graph Moodle
bull Engagement in learning activities
Generates social network forum graph representing studentsrsquo level of communication
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 5167
Indicative Predictive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 Early Warning System BrightBytes
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
2 Student Success System Desire2Learn
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
bull Demographics
Generates reports of each studentrsquos performance patterns and predicts future performance trends
3 X-Ray Analytics BlackBoard - Moodlerooms
bull Engagement in learning activities
bull Performance in assessment activities
Generates reports of each studentrsquos performance patterns and predicts future performance trends
4 Engagement analytics Moodle
bull Engagement in learning activities
bull Performance in assessment activities Predicts future performance trends and risk of failure
5 Analytics and Recommendations Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Predicts studentsrsquo final grade
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 5267
Indicative Prescriptive Learning Analytics Tools
Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
bull Engagement in learning activities
bull Performance in assessment activities
bull Behavioural data
Generates reports on studentsrsquo performance trends and provides recommendations for assessment activities
2 FishTree FishTree
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational resources
3 LearnSmart McGraw-Hill
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for learning and assessment activity pathways as well as educational resources
4 Adaptive Quiz Moodle bull Performance in assessment activities Provides recommendations for assessment activities
5 Analytics and Recommendations
Moodle
bull Engagement in learning activities
bull Performance in assessment activities
bull Interaction with Digital Educational Resources and Tools
Generates reports on studentsrsquo performance trends and provides recommendations for educational activities to engage with
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 5367
Teaching and Learning Analytics to support
Teacher Inquiry
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 5467
Reflective practice for teachers Reflective practice can be defined as[13]
ldquo[A process that] involves thinking about and critically analyzing ones actions with the goal of improving ones professional practicerdquo
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 5567
Types of Reflective practice Two main types of reflective practice[14]
Letrsquos see how combining Teaching and Learning Analytics can support classroom teachersrsquo reflection-on-action through the process of teacher inquiry
- Takes place while the practice is executed and the practitioner reacts on-the-fly - Teaching Analytics and Learning Analytics mainly support this type of teachersrsquo reflection
Reflection-in-action
- Takes a more systematic approach in which practitioners intentionally review analyse and evaluate their practice after it has been performed documenting the process and results
Reflection-on-action
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 5667
Teacher Inquiry (12)
bull Teacher inquiry is defined as[15]
ldquo[a process] that is conducted by teachers individually or collaboratively with the primary aim of understanding teaching and learning in contextrdquo
bull The main goal of teacher inquiry is to improve the learning conditions for students
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 5767
Teacher Inquiry (22)
bull Teacher inquiry typically follows a cycle of steps
Identify a Problem for Inquiry
Develop Inquiry Questions amp Define
Inquiry Method
Elaborate and Document Teaching
Design
Implement Teaching Design and Collect Data
Process and Analyze Data
Interpret Data and Take Actions
The teacher develops specific questions to investigate
Defines the educational data that need to be collected and the method of their analysis
The teacher defines teaching and learning process to be implemented during the
inquiry (eg through a lesson plan)
The teacher makes an effort to interpret the analysed data and takes action in relation to their
teaching design
The teacher processes and analyses the collected data to obtain insights related to the
defined inquiry questions
The teacher implements their classroom teaching design and
collects the educational data
The teacher identifies an issue of concern in the teaching practice which will be
investigated
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 5867
Teacher Inquiry Needs
Teacher inquiry can be a challenging and time consuming process for individual teachers
‒ Heavy workloads allow limited time for reflection on teaching practice ‒ Increased difficulty when done in isolation from other teachers
Digital technologies can be used to support teacher inquiry ‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of inquiry
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 5967
Teaching and Learning Analytics
bull Teaching and Learning Analytics (TLA) aim to combine ndash The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem develop specific questions to guide inquiry and to document the teaching design
ndash The data collection processes and analytical capabilities of Learning Analytics to make sense of studentsrsquo data in relation to the teaching design elements and help the teacher to take action
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 6067
Teaching and Learning Analytics to support Teacher Inquiry
bull TLA can support teachers engage in the teacher inquiry cycle
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse the teaching design and help the teacher to bull pinpoint the specific elements of their teaching design
that relate to the problem they have identified bull elaborate on their inquiry question by defining
explicitly the teaching design elements they will monitor and investigate in their inquiry
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data bull Learning Analytics can be used to collect the student
data that the teacher has defined to answer their question
bull Learning Analytics can be used to analyse and report on the collected data in order to facilitate interpretation
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics can be used to map the analysed data to the initial teaching design answer the inquiry question and generate insights for teaching design revisions
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 6167
Indicative Teaching and Learning Analytics Tools
Venture Logo Tool Venture Description
1 LeMo LeMo Project
bull Generates visualisations of the frequency that each learning activity and educational resourcetool have been accessed
bull Generates dashboards to show the navigation paths that students took when engaging with the learning activities and educational resourcestools
2 The Loop Tool Blackboard Moodle
Generates dashboards to visualize how when and to what extend the students have engaged with the learning and assessment activities as well as with the educational resources
3 Quiz statistics Moodle Analyses each assessment activity in terms of various metrics to support their refinement
4 Heatmap tool Moodle Generates visual color-coded reports that show how much each learningassessment activity or educational resourcetool was accessed by the students
5 Events Graphic Moodle Generates dashboards that show the most frequent actions that the students performed
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 6267
Relevant Publications bull S Sergis and D Sampson ldquoTeaching and Learning Analytics a Systematic Literature Reviewrdquo in Alejandro Pentildea-Ayala (Eds) Learning
analytics Fundaments applications and trends ndash A view of the current state of the art Springer 2017 bull S Sergis E Papageorgiou P Zervas D Sampson and L Pelliccione ldquoEvaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plansrdquo in Ann Marcus-Quinn and Triona Hourigan (Eds) Handbook for Digital Learning in K-12 Schools Springer Chapter 11 2017
bull I Pappas MN Giannakos M L Jaccheri and D G Sampson ldquoUnderstanding Studentsrsquo Retention in Computer Science Education The Role of Environment Gains Barriers and Usefulnessrdquo Education and Information Technologies Springer 2017
bull M N Giannakos D G Sampson Ł Kidziński and A Pardo ldquoEnhancing Video-Based Learning Experience through Smart Environments and Analyticsrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull I O Pappas M N Giannakos D G Sampson ldquoMaking Sense of Learning Analytics with a Configurational Approachrdquo in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning 2016
bull S Sergis and D Sampson Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM Education 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016) 2016
bull S Sergis and D Samson ldquoLearning Objects Recommendations for Teachers based on elicited ICT Competence Profilesrdquo IEEE Transactions on Learning Technologies 2016
bull S Sergis and D Sampson School Analytics A Framework for Supporting School Complexity Leadership in J M Spector D Ifenthaler D Sampson and P Isaias (Eds) Competencies Challenges and Changes in Teaching Learning and Educational Leadership in the Digital Age Springer 2016
bull S Sergis and D Sampson Data Driven Decision Support For School Leadership Analysis Of Supporting Systems in Ronghuai Huang Kinshuk and Jon K Price (Eds) ICT in education in global context comparative reports of K-12 schools innovation Springer 2016
bull S Sergis P Zervas and D Sampson ldquoA Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptakerdquo International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4) 33-46 2015
bull P Zervas and D Sampson Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Studentsrsquo Problem Solving Competence Development The Inspiring Science Education Tools in Ronghuai Huang Nian-Shing Chen and Kinshuk (Eds) Authentic Learning through Advances in Technologiesrdquo Springer 2015
bull S Sergis and D Sampson From Teachersrsquo to Schoolsrsquo ICT Competence Profiles in D Sampson D Ifenthaler J M Spector and P Isaias (Eds) Digital Systems for Open Access to Formal and Informal Learning Springer ISBN 978-3-319-02263-5 Chapter 19 pp 307-327 2014
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 6367
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 6467
WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth Western Australia
Digital Learning Research Track httpwwwwww2017comau
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 6567
ICALT2017
Τhe 17th IEEE International Conference on Advanced Learning Technologies
3-7 July 2017 Timisoara Romania European Union
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 6667
谢谢
Thank you
wwwask4researchinfo
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
17 11 2016 6767
References 1 Mandinach E (2012) A Perfect Time for Data Use Using Data driven Decision Making to Inform Practice Educational
Psychologist 47(2) 71-85 2 Lai M K amp Schildkamp K (2013) Data-based Decision Making An Overview In K Schildkamp MK Lai amp L Earl
(Eds) Data-based decision making in education Challenges and opportunities Dordrecht Springer 3 Mandinach E amp Gummer E (2013) A systemic view of implementing data literacy in educator preparation
Educational Researcher 42 30ndash37 4 Means B Chen E DeBarger A amp Padilla C (2011) Teachers Ability to Use Data to Inform Instruction Challenges
and Supports Office of Planning Evaluation and Policy Development US Department of Education 5 Marsh J Pane J amp Hamilton L (2006) Making Sense of Data-Driven Decision Making in Education RAND
Corporation 6 Bienkowski M Feng M amp Means B (2012) Enhancing teaching and learning through educational data mining and
learning analytics An issue brief US Department of Education Office of Educational Technology 1-57 7 NMC (2011) The Horizon Report ndash 2011 Edition 8 SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge 9 Butt G (2008) Lesson Planning (3rd Edition) New York Continuum 10 Sergis S Papageorgiou E Zervas P Sampson D amp Pelliccione L (2016) Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans In AMarcus-Quinn amp T Hourigan (Eds) Handbook for Digital Learning in K-12 Schools (pp 173-189) Springer Chapter 11
11 Data Quality Campaign (2014) What is student data 12 Learning Analytics Community Exchange (2014) Learning Analytics 13 Imel S (1992) Reflective Practice in Adult Education ERIC Digest No 122 14 Schon D (1983) Reflective Practitioner How Professionals Think in Action New York Basic Books 15 Stremmel A (2007) The Value of Teacher Research Nurturing Professional and Personal Growth through Inquiry
Voices of Practitioners 2(3) National Association for the Education of Young Children
