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Keynote at the 10th JTEL Summer School 2014 in Malta Ralf Klamma ACIS Group @ RWTH Aachen University April 28, 2014
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Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 1
Learning Layers
This slide deck is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
Community Learning Analytics –
A New Research Field in TEL
Ralf Klamma Advanced Community Information Systems (ACIS)
RWTH Aachen University, Germany [email protected]
JTEL Summer School, Malta, April 28, 2014
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 2
Learning Layers
Abstract Learning Analytics has become a major research area recently. In particular learning institutions seek ways to collect, manage, analyze and exploit data from learners and instructors for the facilitation of formal learning processes. However, in the world of informal learning at the workplace, knowledge gained from formal learning analytics is only applicable on a commodity level. Since professional communities need learning support beyond this level, we need a deep understanding of interactions between learners and other entities in community-regulated learning processes - a conceptual extension of self-regulated learning processes. In this presentation, we discuss scaling challenges for community learning analytics and give both conceptual and technical solutions. We report experiences from ongoing research in this area, in particular from the two EU integrating project ROLE (Responsive Open Learning Environments) and Learning Layers (Scaling up Technologies for Informal Learning in SME Clusters).
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 3
Learning Layers
Responsive Open
Community Information
Systems
Community Visualization
and Simulation
Community Analytics
Community
Support
Web Analytics W
eb E
ngin
eerin
g
Advanced Community Information Systems (ACIS) Group @ RWTH Aachen
Requirements Engineering
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 4
Learning Layers
Agenda
Lear
ning A
nalyt
ics
Comm
unity
Lear
ning A
nalyt
ics
ROLE
& Le
arnin
g Lay
ers
Expe
rts in
Com
munit
y Info
rmati
on
Syste
ms
Conc
lusion
s & O
utloo
k
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 5
Learning Layers
A PHD STUDENT VIEW ON THE RESEARCH FIELD
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 6
Learning Layers Motivations for Doing
PhD Research in TEL ■ Some reasons (more?)
– My supervisor told me … (research interest of person paying me) – My own research interest – Good career perspectives (get famous, get rich, or both)
■ Formal Learning – Close to my own practice and experience as a teacher, researcher – Research settings easier to control (classroom as a lab)
■ Informal Learning – Better funding opportunities (H2020, industry) – More innovative (mobile, Web, micro, games) – Real impact expected
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 7
Learning Layers
LEARNING ANALYTICS
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 8
Learning Layers Self- and Community Regulated
Learning Processes
Based on [Fruhmann, Nussbaumer & Albert, 2010]
Learner profile information is
defined or revised
Learner finds and selects learning resources
Learner works on selected learning resources
Learner reflects and reacts on
strategies, achievements and usefulness
plan
learnreflect
The Horizon Report – 2011 Edition
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 9
Learning Layers The Long Tail of Personal Knowledge
in Lifelong Learning
■ Zillions of new learning opportunities ■ Abundance of learning materials ■ But: Extremely challenging to find & navigate
High-quality, specially designed, learning materials like books or course material
Gaps in personal knowledge identified mostly by real-world practice
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 10
Learning Layers
Personal Learning Environment (PLE) PLE describes the tools, communities, and services that constitute the individual educational platforms learners use to direct their own learning and pursue educational goals LMS – course-centric vs. PLE – learner-centric:
• Extension of individual research • Students in charge of their learning process
• self-direction, responsibility • Promotes authentic learning (incorporating expert feedback) • Student’s scholarly work + own critical reflection + the work and voice of others • Web 2.0 influence on educational process
• customizable portals/dashboards, iGoogle, My Yahoo! • Learning is a collaborative exercise in collection, orchestration, remixing, & integration of data into knowledge building • Emphasis on metacognition in learning
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 11
Learning Layers ROLE Approach to the Design
of Learning Experiences
guidance & freedom of
learner
motivation of learner (intrinsic,
extrinsic)
stimulation of learner’s meta-
cognition
collaboration & good practice sharing among
peers
personalization & adaptability to learner & context What is the impact of these
findings from behavioral & cognitive psychology on
design of learning?
Goal setting Planning Reflection
Control & Responsibility Recommendation
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 12
Learning Layers ROLE Approach to the Design
of Learning Experiences What is the impact of these findings from behavioral & cognitive psychology on
design of Personal Learning Environments?
learner profile information is defined and revised
learner finds and selects learning resources
learner works on selected learning resources
plan
learn reflect
learner input regarding goals, preferences, …
creating PLE
recommendations from peers or tutors
assessment and self-assessment
evaluation and self-evaluation
feedback (from different sources)
learner should understand and control own learning process
ROLE infrastructure should provide adaptive guidance
attaining skills using different learning events (8LEM)
learner reflects and reacts on strategies, achievements,
and usefulness
monitoring recommen-dations
be aware of
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Prof. Dr. M. Jarke 13
Learning Layers Learning Analytics vs. Community
Learning Analytics Formal Learning Learning Analytics Community
Regulated Learning
Community Learning Analytics
Environment LMS EDM/VA CIS/ROLE DM/VA/SNA/Role Mining
Tools Fixed LMS Specific Eco-System Tool Recommender
Activities Fixed Content Recommender
Dynamic Content Recommender / Expert Recommender
Goals Fixed Progress Dynamic Progess / Goal Mining / Refinement
Communities Fixed Not applicable Dynamic (Overlapping) Community Detection
Use Cases Courses Learning Paths Peer Production / Scaffolding
Semantic Networks of Learners / Annotations
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Learning Layers
COMMUNITY LEARNING ANALYTICS – A GENERAL APPROACH
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Learning Layers
Communities of Practice ■ Communities of practice (CoP) are groups of people
who share a concern or a passion for something they do and who interact regularly to learn how to do it better (Wenger, 1998)
■ Characterization of experts in CoP – Shared competence in the domain – Shared practice over time by interactions – Expertise based on gaining and having reputation within the CoP – Being an expert vs. being a layman, a newcomer, an amateur etc. – Informal leadership – Identity as an expert depends on the lifecycle of the communities
Expertise in highly dynamic, locally distributed multi-disciplinary and heterogeneous communities?
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Prof. Dr. M. Jarke 16
Learning Layers Proposed Development of the
Community Learning Analytics Field ■ Will happen J Big Data by Digital Eco Systems (Quantitative Analysis)
– A plethora of targets (Small Birds) – Professional Communities are distributed in a long tail – Professional Communities use a digital eco system
– An arsenal of weapons (Big Guns) – A growing number of community learning analytics methods – Combined methods from machine intelligence and knowledge representation
■ May not happen L Deep Involvment with community (Qualitative Analysis) – Domain knowledge for sense making – Passion for community and sense of belonging – Community learns as a whole
→ Community Learning Analytics for the Community by the Community
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Learning Layers
Web 2.0 Competence Development Cultural and Technological
Shift by Social Software Impact on
Knowledge Work Impact on
Professional Communities
Web 1.0 Web 2.0 Microcontent Providing
commentary Personal knowledge
publishing Establishing personal
networks Testing Ideas
Social learning Identifying competences Emergent Collaboration
Trust & Social capital
personal website and content management
blogging and wikis User generated content Participation
directories (taxonomy) and stickiness
Tagging ("folksonomy") and syndication
Ranking Sense-making
Remixing Aggregation Embedding
Emergent Metadata Collective intelligence Wisdom of the Crowd Collaborative Filtering Visualizing Knowledge
Networks
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Prof. Dr. M. Jarke 18
Learning Layers Interdisciplinary Multidimensional
Model of Communities ■ Collection of CoP Digital Traces in a MediaBase
– Post-Mortem Crawlers – Real-time, mobile, protocol-based (MobSOS) – (Automatic) metadata generation by Social Network Analysis
■ Social Requirements Engineering with i* Framework for defining goals and dependencies in CoP
Social Software Cross-Media Social Network Analysis on Wiki, Blog, Podcast, IM, Chat, Email, Newsgroup, Chat …
Web 2.0 Business Processes (i*) (Structural, Cross-media)
Members (Social Network Analysis: Centrality,
Efficiency, Community Detection)
Network of Artifacts Content Analysis on Microcontent, Blog entry, Message,
Burst, Thread, Comment, Conversation, Feedback (Rating)
Network of Members
Communities of practice
Media Networks
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Prof. Dr. M. Jarke 19
Learning Layers Community Learning Analytics
in CoP ■ User-to-Service Communication
• CoP-aware Usage Statistics • Identification of successful CoP services • Identification of CoP service usage patterns
■ User-to-User Communication • CoP-aware Social Network Analysis • Identification of influential CoP members • Identification of CoP member interaction/learning patterns
+
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Learning Layers Supporting Community Practice
with the MobSOS Success Model
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Prof. Dr. M. Jarke 21
Learning Layers Community SRE Processes–
i* Strategic Rationale
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Learning Layers
RESPONSIVE OPEN LEARNING ENVIRONMENTS
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Prof. Dr. M. Jarke 23
Learning Layers Responsive Open Learning
Enviroments (ROLE) 2009-2012
• Empower the learner to build their own responsive learning environment ROLE Vision
• Awareness and reflection of own learning process Responsiveness
• Individually adapted composition of personal learning environment User-Centered
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Prof. Dr. M. Jarke 24
Learning Layers ROLE
Technical Infrastructure
■ Sucessfully deployed in industry and education ■ Open Source Software Development Kit ■ ROLE Widget Store (role-widgetstore.eu) ■ ROLE Sandbox (role-sandbox.eu)
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Prof. Dr. M. Jarke 25
Learning Layers ROLE Sandbox – Geospatial &
Temporal Access
§ Users: 5787 (95% external) § Widgets: 1475 (71.5% external) § Spaces: 1283 (64.3% external) § Shared Resources: 18922 (6% external)
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Learning Layers
ROLE Requirements Bazaar – Community-aware Requirements Prioritization
Factors influencing requirements ranking
User-controlled weighting of ranking factors
Community-dependent requirements ranking lists
http://requirements-bazaar.org
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Learning Layers Learning Analytics Visualization –
Dashboards
1. Database Selection
2. Filter Selection/Definition
3. Adapted Visualization
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Prof. Dr. M. Jarke 28
Learning Layers
LEARNING LAYERS – SCALING UP TECHNOLOGIES FOR INFORMAL LEARNING IN SME CLUSTERS
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 29
Learning Layers
Maturing
Interacting with People at the workplace Paul discovers a problem at the construction site with PLC equipment ...
Generating dynamic Learning Material The regional training center observes the Q&A and links it to their course material ...
Q: How to use PLC equipment …? • I have seen this before here … • Last time I did it, I … • Here is something helpful
Social Semantic Layer Emerging shared meaning,
giving context Energy Consump.on
Lightning
X3-‐PVQ X3-‐PJC
X3-‐POZ PLC Equipment Instructional Taxonomy
• What is … • How to … • Example of …
Tutorial: How to Use PLC What is PLC How to use it? Examples Further Information Hot Questions and Answers
Work Practice Taxonomy • Installation • Testing • Operation
Peter
Paul
Mary
Interacting in the Physical Workplace Physical workplace is equipped with QR tags, learning materials are delivered just in time ...
A list of helpful resources • Tutorials: How to use … • Persons: Peter, Mary, … • Work Practice: Installation,.. • Concepts: PLC, Lightning • Q&A: …,
Learning Layers in the Construction Industry
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 30
Learning Layers
Learning Layers – Scaling Technologies for Informal Learning
Learning Layers – Scaling up Technologies for Informal Learning in SME Clusters
Lehrstuhl Informatik 5 (Information Systems)
Prof. Dr. M. Jarke 31
Learning Layers
Space (shared by multiple users)
Using the ROLE Framework for Semantic Video Annotation
Web application (composed of widgets)
Widget (collaborative web component)
http://role-sandbox.eu/
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Prof. Dr. M. Jarke 32
Learning Layers
SeViAnno Prototypes SeViAnno (Web)
SeViAnno 2.0 (Widgets)
AnViAnno (Android)
AchSo! (Android)
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Prof. Dr. M. Jarke 33
Learning Layers
COMMUNITY LEARNING ANALYTICS – EXPERT IDENTIFICATION
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Prof. Dr. M. Jarke 34
Learning Layers Experts in
Learning Communities ■ In learning communities
many experts from different fields meet – Intergenerational learning – Interdisciplinary learning
■ New Openness for Amateur Contributions
■ Methods, Tools & CoP co-develop – Expert role models needed – Expert identification based
on complex media traces
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Prof. Dr. M. Jarke 35
Learning Layers YouTell - A Web 2.0 Service for
Collaborative Storytelling § Collaborative storytelling § Web 2.0 Service § Story search and
“pro-sumption”
§ Tagging § Ranking/Feedback § Expert finding § Recommending
Klamma, Cao, Jarke: Storytelling on the Web 2.0 as a New Means of Creating Arts Handbook of Multimedia for Digital Entertainment and Arts, Springer, 2009
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Learning Layers Expert Finding – Computation of
Actual Knowledge ■ Data vector consists of
– Personal data vector – Competences, skills,
qualification profile – Self-entered data
– Story data vector – Visits of stories – Involvement in projects
– Expert data vector – Advice given – Advice received
– Value = #Keywords � Date Decay � Feedback
Motivation PESE: Web 2.0 –Anwen- dung für community- basiertes Storytelling Der PESE- Prototyp Evaluierung des Prototypen Zusammen- fassung Ausblick
Find the most appropriate expert
Data vector represents knowledge of the expert
Lehrstuhl Informatik 5 (Information Systems)
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Learning Layers Knowledge-Dependent
Learning Behaviour in Communities
Renzel, Cao, Lottko, Klamma: Collaborative Video Annotation for Multimedia Sharing between Experts and Amateurs, WISMA 2010, Barcelona, Spain, May 19-20, 2010
§ Expert finding algorithm: Knowledge value of community sorted by keywords § Community behavior: Experts spent more time on the services § Experts prefers semantic tags while amateurs uses “simple” tags frequently § Community tags: Experts use more precise tags
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Learning Layers
Threads to Expert Finding ■ Compromising techniques
— Sybil attack [Douc 2002], Reputation theft, Whitewashing attack, etc.. — Compromising the input and the output of the expert identification algorithm
■ Example: Sybil attacks — Fundamental problem in open collaborative Web systems — A malicious user creates many fake accounts (Sybils) which all reference the user to
boost his reputation (attacker’s goal is to be higher up in the rankings)
Sybil region Honest region ABack edges
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Prof. Dr. M. Jarke 39
Learning Layers
Conclusions & Outlook
■ Community Learning Analytics – Informal learning more challenging for learning analytics – New research challenges and funding opportunities – Highly interdisciplinary and multi-method research
■ Case Studies – Responsive Open Learning Environments – ROLE SDK for Near Real-Time Widget-Based Web Applications
– Learning Layers - Scaling up Technologies for Informal Learning in SME Clusters – Informal Learning on the Workplace – Collaborative Semantic Video Annotation