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1 st. A nnual P rogress S eminar. Teaching-Learning of Visual Analytics. Rwitajit Majumdar. 1 st step towards doctoral research. Under supervision of Prof. Sridhar Iyer Prof. Aniruddha J oshi. CS 101 Engagement study. ET 802Research Project Credit ………………… RM - PowerPoint PPT Presentation
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Annual ProgressSeminar
1st
1st step towards doctoral research
Under supervision ofProf. Sridhar Iyer
Prof. Aniruddha Joshi
Rwitajit Majumdar
Teaching-Learning of Visual Analytics
JAN feb mar apr may jun julyaug sep oct nov dec
JAN
ET 802 Research ProjectCredit…………………RM
ETS801 Seminar: Hand skills Teaching & Learning CreditID405 Human Computer Interaction
sit through…………HCI
ET 801 Introduction to Educational TechnologyCredit………………..Intro ETHS 699 Communication and Presentation skill Non-Credit………..HSSID 665 Craft, Creativity and Post Modernism Credit..................CCIN 609 Visual Design for Interactive Systems sit through………..VD
CS 101 Engagement study
LAMP – Large Scale Addressal of Muddy PointsPULSE – Protocol oriented Utility for Logging Student Engagement
Advance topics in Cognition
Research Question
Methodology• Sample• Design• Variables• Analysis
Results• Compute• Represent
Interpret
Generic problem in research
Research Question
Methodology• Sample• Design• Variables• Analysis
Results• Compute• Represent
Interpret
1312 words extracted from the titles of 165 research article published in Computers & Education in 2013
http://timc.idv.tw/wordcloud/
1. What are the topics?2. What did the figures &
tables convey?
Educational environments and technology
Distance education and learning
Multimedia Education
Education and performance
Teacher Education
design & development
systemic change
research & theory
others
0 10 20 30 40 50 60 70
58
1
22
18
2
23
1
38
2
Kucuk, S., Aydemir, M., Yildirim, G., Arpacik, O., Goktas, Y., “Educational technology research trends in Turkey from 1990 to 2011” Computers & Education 68 (2013) 42–50
N=165
Research Question
Methodology• Sample• Design• Variables• Analysis
Results• Compute• Represent
Interpret
Research Question
Methodology• Sample• Design• Variables• Analysis
Results• Compute• Represent
Interpret
133
figures tables
7 22 3
demographics
statistics
description and example
specific result
lit review
0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00 400.00 450.00 500.00
51.00
445.00
122.00
132.00
20.00
Distribution of Tables
bar; 65scatter plot; 11
line; 33pi and
venn; 7
model; 107
specific diagrams / fig; 166
screen shots; 176
photo-graphs; 42
others; 21
Distribution of Figures
C-mapOntologyHistogramCircleBox plot
What am I trying to establish?
leverageProper usage of Tables and Figures evidence to support claims in the study
Different studies use different graphs scopeas designer: evaluate
as trainer: implications on teaching-learning
Different tools have different affordances
Difference in interpretation?
Educational Technology Research
Student Engagement1 Student Learning2
Technology Enabled Learning Metric
Accessibility
Effectiveness
Attractiveness
EfficiencyET
1. Aditi Kothiyal, Rwitajit Majumdar, Sahana Murthy and Sridhar Iyer. Effect of think-pair-share in a large CS1 class: 83% sustained engagement. ACM Intl Computing Education Research Workshop (ICER), San Diego, USA, August 2013.
2. Shitanshu Mishra and Sridhar Iyer. Problem Posing Exercies (PPE): An instructional strategy for learning of complex material in introductory programming courses. IEEE Intnl Conf on Technology for Education (T4E), Kharagpur, India, Dec 2013.
Behaviors
How it changed?Problem posing skillHow does it affect learning?
Student Engagement1 Student Learning2
1. Aditi Kothiyal, Rwitajit Majumdar, Sahana Murthy and Sridhar Iyer. Effect of think-pair-share in a large CS1 class: 83% sustained engagement. ACM Intl Computing Education Research Workshop (ICER), San Diego, USA, August 2013.
2. Shitanshu Mishra and Sridhar Iyer. Problem Posing Exercies (PPE): An instructional strategy for learning of complex material in introductory programming courses. IEEE Intnl Conf on Technology for Education (T4E), Kharagpur, India, Dec 2013.
N 450
Batch of 2013
Classroom Observations during Think-Pair-Share activity: Behaviors
Pre test – Post testUnstructured interviews
Observed student’s behavior
The difference in the distribution of Pre-Test
vsPost-Test
was not statistically significant
But, the unstructured interview had evidence that the problem posing activity was engaging, non-trivial and interesting to work out.
In order to understand the dynamics further nature of problem created and the Pre-Post test performance
was studied
Often the process of interaction is studied but the tracking across the process is not done.
Polarized operations:Aggregate statistics evens out the rich variation in dataTracking individual parameter and explicating trends are often difficult to
structure
Requires1. A structure for analysis2. Representation to explicate patterns in the data
Some approach that can group the sample according to set criteria, that the researcher focus to study,
and study their migration
What exists:
Sophisticated Time-series and Cluster Analysis.
else
• Researchers calculates distribution for each phase of tracking • Represent it through pie/bar chart• Calculates how the sample dynamics change on certain parameter
• Writes an elaborate paragraph to explain trends.
SAT Diagram is a unified graph
representing distribution of stratified categories
based on attributes of collected data as its nodes,
which are then tracked along different phases of
any activity for a given sample.
Between each phase it is a complete bipartite graph.
Stratified Attribute Tracking Diagram
Activity: The event for which the data is logged.
Phase: The different parts of the activities that is analysed.
Strata: Group formed out of the sample based on the predefined criteria of the attribute value.
Tr(imi+1n) indicates the t-ratio between group ‘m’ in phase ‘i’ group ‘n’ in phase ‘i+1’.
It is calculated as the ratio of the sample size that migrates between phase-i-group-m and phase-i+1-group-n to the initial sample size of phase-i-group-m.
Stratum distribution: of a certain group in a phase is the ratio of number of people in that group to the sample population.
No. Study Phase 1 Phase 2 Phase 3
1 Quantifying Student Engagement
Name Think Pair Share
Attributes Student Behaviors
Strata 1. Fully engaged 2. Mostly engaged 3. Sometimes engaged 4. Never engaged
No. Study Phase 1 Phase 2 Phase 3
2 Tracking Student’s problem posing ability
Name Pre test Question Quality Post test
Attributes Score Rubric difficulty Score Score
Strata 1. High2. Medium3. Low
No. Study Phase 1 Phase 2 Phase 3
2 Tracking Student’s problem posing ability
Name Pre test Question Quality Post test
Attributes Score Rubric difficulty Score Score
Strata 1. High2. Medium3. Low
No. Study Phase 1 Phase 2
2 Tracking Student’s problem posing ability
Name Pre test Post Test
Attributes Score Score
Strata 1. High2. Medium3. Low
No. Study Phase 1 Phase 2
3 Frustration instances and motivational message in an ITS
Name Without motivational message
With motivational message
Attributes Number of frustration instances per session
Strata 1. 2 to 32. 4 to 5 3. 6
1. 0 to 12. 2 to 33. 4 to 54. >6
No. Study Phase 1 Phase 2
4 Tracking Mentee performance
Name Idea Planning Study Planning
Attributes Rubric score
Strata 1. High 2. Medium3. Low4. Very low
No. Study Phase 1 Phase 2
5 Student’s perception on LAMP framework
Name Question Posing Receiving Answer
Attributes Likert scale Perception
Strata 1. Agree 2. Neutral3. Disagree
No. Study Phase 1 Phase 2 Phase 3
1 Quantifying Student Engagement
Name Think Pair Share
Attributes Student Behaviors
Strata 1. Fully engaged 2. Mostly engaged 3. Sometimes engaged 4. Never engaged
2 Tracking Student’s problem posing ability
Name Pre test Question Quality Post test
Attributes Score Rubric difficulty Score
Score
Strata 1. High2. Medium3. Low
3 Frustration instances and motivational message in an ITS
Name Without motivational message
With motivational message
Attributes Number of frustration instances per session
Strata 1. 2 to 32. 4 to 5 3. 6
1. 0 to 12. 2 to 33. 4 to 54. >6
4 Tracking Mentee performance
Name Idea Planning Study Planning
Attributes Rubric score
Strata 1. High 2. Medium3. Low4. Very low
5 Student’s perception on LAMP framework
Name Question Posing Receiving Answer
Attributes Likert scale Perception
Strata 1. Agree 2. Neutral3. Disagree
Application
Exploratory Analysis
Presented a student’s engagement model.
Confirmatory Analysis
Confirmation of the qualitative data collected by zooming in the statistical distribution
Represent trends
Decrease in frustration levels
Represent trends
Increase in mentee performance
Exploratory Analysis
The perception trend is checked to further investigate
19 slides of discussion
Issues
While presenting how should it highlight the trend which the researcher wants to report?
PART 2
REVIEW PART 1
demographics
statistics
description and example
specific result
lit review
0 50 100 150 200 250 300 350 400 450 500
51
445
122
132
20
Distribution of Tables
Which of this is more effective?
demographics; 51.00
statistics; 445.00
description and example;
122.00
specific result; 132.00
lit review; 20.00
demographics
statisti
cs
description and exa
mple
specific r
esult
lit revie
w0
50100150200250300350400450500
51
445
122 132
20
How to plot this data-type?
PART 2
REVIEW PART 1&
My Doctoral research path
Data Visualization Designer Visual Analytics Trainer
2. How can visual analytics be integrated with existing analysis workflow for educational researchers?
A. What are the advantages of such a modified workflow of analysis?i. Does it provide new insights?ii. Does it make the analysis and interpretation ‘easier’?iii. Does it assist any other cognitive operation preceding a decision making task?
1. In a particular research designs apart from indicating significance of statistical difference what more relevant information can we explicate from the collected data?
A. What is the nature of effective representations for conveying educational research datasets?i. What is the kind of questions asked on Educational Datasets?ii. What is the current trend in reporting evidence to support the research?
Data Visualization DesignerResearch Questions
1. During the training session of data visualization process to an educational researcher:A. what are the modules that are in scope?B. what instructional strategies is effective for a contact workshop?
Visual Analytics Trainer
2. What are the effects of affordances that the visualization tool provides on developing skillset of that tool?
A. Is there scope of development of alternate conception about topics of effective visualization because of the affordances in the tool?
3. What cognitive model can help understand the operations during data visualization and interpreting visual representation?
Research Questions
JAN feb mar apr may jun julyaug sep oct nov dec
JAN
• Refine SAT diagram.• Identifying theoretical framework to define effectiveness and efficiency for SAT diagram
to explicate insights in data.• Investigate applicability to other ET research designs.• Check Visualization course and find concepts relevant to apply and
find solution to educational research problems.
• Developing application for generating SAT diagram• Developing teaching learning strategies to develop skillset for using visualizing tools.• Conducting a visual data representation workshop for educational dataset.
Data Representation?
“Here is my secret. It is very simple. It is only with the heart that one can see rightly
What is essential is invisible to the eye.”
- Antoine de Saint Exupéry
Thank you