A nnual P rogress S eminar

1. 1st Annual Progress Seminar Teaching-Learning of Visual Analytics RwitajitMajumdar 1st step towards doctoral research Under supervision of Prof. Sridhar Iyer Prof. AniruddhaJoshi

2. CS 101 Engagement study ET 802 Research Project Credit…………………RM ETS801 Seminar: Hand skills Teaching & Learning Credit ID405 Human Computer Interaction sit through…………HCI Advance topics in Cognition JANfeb marapr may junjulyaugsepoctnovdecJAN ET 801 Introduction to Educational Technology Credit………………..Intro ET HS 699 Communication and Presentation skill Non-Credit………..HSS ID 665 Craft, Creativity and Post Modernism Credit..................CC IN 609 Visual Design for Interactive Systems sit through………..VD LAMP – Large Scale Addressal of Muddy Points PULSE – Protocol oriented Utility for Logging Student Engagement

3. Generic problem in research

5. http://timc.idv.tw/wordcloud/ What are the topics? What did the figures & tables convey? 1312 words extracted from the titles of165 research article published in Computers & Education in 2013

6. 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

10. tables figures 7 133 22 3

11. Distribution of Tables

12. C-map Ontology Histogram Circle Box plot Distribution of Figures

13. What am I trying to establish? Difference in interpretation? as designer: evaluate as trainer: implications on teaching-learning scope Different tools have different affordances Different studies use different graphs Proper usage of Tables and Figures evidence to support claims in the study leverage

14. Technology Enabled Learning Metric Attractiveness Effectiveness Efficiency ET Accessibility Student Engagement1 Student Learning2 Behaviors Problem posing skill How it changed? How does it affect learning? Educational Technology Research AditiKothiyal, RwitajitMajumdar, 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. ShitanshuMishra and Sridhar Iyer. Problem Posing Exercies (PPE): An instructional strategy for learning of complex material in introductory programming courses. IEEE IntnlConf on Technology for Education (T4E), Kharagpur, India, Dec 2013.

15. Student Engagement1 Student Learning2 Classroom Observations during Think-Pair-Share activity: Behaviors Pre test – Post test Unstructured interviews N 450 Batch of 2013 AditiKothiyal, RwitajitMajumdar, 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. ShitanshuMishra and Sridhar Iyer. Problem Posing Exercies (PPE): An instructional strategy for learning of complex material in introductory programming courses. IEEE IntnlConf on Technology for Education (T4E), Kharagpur, India, Dec 2013.

16. Observed student’s behavior

17. The difference in the distribution of Pre-Test vs Post-Test was not statistically significant In order to understand the dynamics further nature of problem created and the Pre-Post test performance was studied But, the unstructured interview had evidence that the problem posing activity was engaging, non-trivial and interesting to work out.

18. 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 data Tracking individual parameter and explicating trends are often difficult to structure Requires A structure for analysis 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

19. 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.

21. Stratified Attribute Tracking Diagram 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.

22. Phase: The different parts of the activities that is analysed. 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. • Strata: Group formed out of the sample based on the predefined criteria of the attribute value. • Stratum distribution: of a certain group in a phase is the ratio of number of people in that group to the sample population. • Activity: The event for which the data is logged.

31. 19 slides of discussion 

32. Issues While presenting how should it highlight the trend which the researcher wants to report?

33. PART 2 REVIEW PART 1

34. Distribution of Tables Which of this is more effective?

35. How to plot this data-type?

36. Data Visualization Designer Visual Analytics Trainer PART 2 REVIEW PART 1 & My Doctoral research path

37. Research Questions Data Visualization Designer • In a particular research designs apart from indicating significance of statistical difference what more relevant information can we explicate from the collected data? • What is the nature of effective representations for conveying educational research datasets? • What is the kind of questions asked on Educational Datasets? • What is the current trend in reporting evidence to support the research? • How can visual analytics be integrated with existing analysis workflow for educational researchers? • What are the advantages of such a modified workflow of analysis? • Does it provide new insights? • Does it make the analysis and interpretation ‘easier’? • Does it assist any other cognitive operation preceding a decision making task?

38. Research Questions Visual Analytics Trainer • During the training session of data visualization process to an educational researcher: • what are the modules that are in scope? • what instructional strategies is effective for a contact workshop? • What are the effects of affordances that the visualization tool provides on developing skillset of that tool? • Is there scope of development of alternate conception about topics of effective visualization because of the affordances in the tool? • What cognitive model can help understand the operations during data visualization and interpreting visual representation?

39. 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. JANfeb marapr may junjulyaugsepoctnovdecJAN • 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.

40. “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 Data Representation? Thank you