Educational data mining overview & Introduction to Exploratory Data Analysis with DataShop

1. Educational data mining overview & Introduction to Exploratory Data Analysis with DataShop Ken Koedinger CMU Director of PSLC Professor of Human-Computer Interaction & Psychology Carnegie Mellon University

2. Overview • DataShop Overview • Logging model • DataShop Features • Quantitative models of learning curves • Power law, logistic regression • Contrasting KC models • Exploratory Data Analysis Exercise (start) • Knowledge Component Model Editing

3. Logging & Storage Models • Education technologies are “instrumented” to produce log data • We encourage a standard log format • XML format generalized from Ritter & Koedinger (1995) • Also convert log data from other formats

4. Relational Database -- complex!

5. Example activity generating “click stream” data • Geometry Cognitive Tutor: “Making Cans” problem • Find the area of scrap metal left over after removing a circular area (the end of a can) from a metal square. • Student enters values in worksheet • Tutor provides feedback & instruction • Records student’s actions & tutor responses • Logs stored in files on school server or database at Carnegie Learning • Later imported into DataShop

6. DataShop logging model • Main constructs: • Context message: the student, problem, and session with the tutor • Tool message: represents an action in the tool performed by a student or tutor • Tutor message: represents a tutor’s response to a student action

7. DataShop XML format: Context message Dataset name Course unit <context_message context_message_id="C2badca9c5c:-7fe5" name="START_PROBLEM"> <dataset> <name>Geometry Hampton 2005-2006</name><level type="Lesson"> <name>PACT-AREA</name> <level type="Section"> <name>PACT-AREA-6</name> <problem> <name>MAKING-CANS</name> </problem> </level> </level> </dataset></context_message> Course section Problem

8. DataShop XML format: Tool & Tutor Messages <tool_message context_message_id="C2badca9c5c:-7fe5"> <semantic_event transaction_id="T2a9c5c:-7fe7" name="ATTEMPT" /> <event_descriptor> <selection>(POG-AREA QUESTION2)</selection> <action>INPUT-CELL-VALUE</action> <input>200.96</input> </event_descriptor></tool_message><tutor_message context_message_id="C2badca9c5c:-7fe5"> <semantic_event transaction_id="T2a9c5c:-7fe7" name="RESULT" /> <event_descriptor> … [as above] … </event_descriptor> <action_evaluation>CORRECT</action_evaluation></tutor_message>

9. Example Stored Transactions • Student interactions (or transactions) are stored in a relational database, can be exported as table • Example: Student S01 on Making-Cans problem

10. Transactions • Info for each transaction • student(s), session, time, problem, problem step, attempt number, student action • tutor response, number of hints, knowledge component code • Logging of on-line tools (e.g., a virtual lab) does not include tutor response

11. Step & Transaction Definitions • A problem-solving activity typically involves many tool & tutor messages. • “Steps” represent completion of possible subgoals or pieces of a problem solution • “Transactions” are attempts at a step or requests for instructional help

12. Example: data aggregated by student-step

13. Overview • DataShop Overview • Logging model • DataShop Features • Quantitative models of learning curves • Power law, logistic regression • Contrasting KC models • Exploratory Data Analysis Exercise (start) • Knowledge Component Model Editing

14. DataShop Analysis Tools • Dataset Info • Performance Profiler • Learning Curve • Error Report • Export • Sample Selector

15. Dataset Info • Meta data for given dataset • PI’s get ‘edit’ privileges, others must request it Papers and Files storage Problem Breakdown table Dataset Metrics 15

16. Performance Profiler Multipurpose tool to help identify areas that are too hard or easy • View measures of • Error Rate • Assistance Score • Avg # Hints • Avg # Incorrect • Residual Error Rate • Aggregate by • Step • Problem • KC • Dataset Level

17. Learning Curve Visualizes changes in student performance over time View by KC or Student, Assistance Score or Error Rate Time is represented on the x-axis as ‘opportunity’, or the # of times a student (or students) had an opportunity to demonstrate a KC

18. Error Report • Provides a breakdown of problem information (by step) for fine-grained analysis of problem-solving behavior • Attempts are categorized by student View by Problem or KC

19. Sample Selector Easily create a sample/filter to view a smaller subset of data • Filter by • Condition • Dataset Level • Problem • School • Student • Tutor Transaction Shared (only owner can edit) and private samples

20. Export You can also export the Problem Breakdown table and LFA values! • Two types of export available • By Transaction • By Step • Anonymous, tab-delimited file • Easy to import into Excel!

21. Help/Documentation • Extensive documentation with examples • Contextual by tool/report • http://learnlab.web.cmu.edu/datashop/help Glossary of common terms, tied in with PSLC Theory wiki

22. New Features • Manage Knowledge Component models • Create, Modify & Delete KC models within DataShop • Addition of Latency Curves to Learning Curve Reporting • Time to Correct • Assistance Time • Problem Rollup & Export • Enhanced Contextual Help

23. Overview • DataShop Overview • Logging model • DataShop Features • Quantitative models of learning curves • Power law, logistic regression • Contrasting KC models • Exploratory Data Analysis Exercise (start) • Knowledge Component Model Editing

24. Recall learning curve story Without decomposition, using just a single “Geometry” KC, no smooth learning curve. But with decomposition, 12 KCs for area concepts, a smooth learning curve. Upshot: A decomposed KC model fits learning & transfer data better than a “faculty theory” of mind

25. Learning curve analysis • The Power Law of Learning (Newell & Rosenbloom, 1993) Y = a Xb Y – error rate X – opportunities to practice a skill a – error rate on 1st opportunity b – learning rate After the log transformation “a” is the“intercept” or starting point of the learning curve “b” is the “slope” or steepness of the learning curve

26. More sophisticated learning curve model • Generalized Power Law to fit learning curves • Logistic regression (Draney, Wilson, Pirolli, 1995) • Assumptions • Different students may initially know more or less => use an intercept parameter for each student • Students learn at the same rate => no slope parameters for each student • Some productions may be more known than others => use an intercept parameter for each production • Some productions are easier to learn than others => use a slope parameter for each production • These assumptions are reflected in detailed math model …

27. More sophisticated learning curve model p  Probability of getting a step correct (p) is proportional to: • if student i performed this step = Xi, add overall “smarts” of that student = i • if skill j is needed for this step = Yj, add easiness of that skill = jadd product of number of opportunities to learn = Tj & amount gained for each opportunity = j Use logistic regression because response is discrete (correct or not) Probability (p) is transformed by “log odds” “stretched out” with “s curve” to not bump up against 0 or 1 (Related to “Item Response Theory”, behind standardized tests …)

28. Different representation, same model • Predicts whether student is correct depending on knowledge & practice • Additive Factor Model (Draney, et al. 1995, Cen, Koedinger, Junker, 2006)

29. The Q Matrix How to represent relationship between knowledge components and student tasks? Tasks also called items, questions, problems, or steps (in problems) Q-Matrix (Tatsuoka. 1983) 2* 8 is a single-KC item 2*8 – 3 is a conjunctive-KC item, involves two KCs 29

30. Model Evaluation • How to compare cognitive models? • A good model minimizes prediction risk by balancing fit with data & complexity (Wasserman 2005) • Compare BIC for the cognitive models • BIC is “Bayesian Information Criteria” • BIC = -2*log-likelihood + numPar * log(numOb) • Better (lower) BIC == better predict data that haven’t seen • Mimics cross validation, but is faster to compute 30

31. Data: the Geometry Area Unit • 24 students, 230 items, 15 KCs 31

32. Learning curve constrast in Physics dataset …

33. Not a smooth learning curve -> this knowledge component model is wrong. Does not capture genuine student difficulties.

34. More detailed cognitive model yields smoother learning curve. Better tracks nature of student difficulties & transfer (Few observations after 10 opportunities yields noisy data)

35. Better than simpler Single-KC model And better than more complex Unique-step (IRT) model Best BIC (parsimonious fit) for Default (original) KC model

36. Overview • DataShop Overview • Logging model • DataShop Features • Quantitative models of learning curves • Power law, logistic regression • Contrasting KC models • Exploratory Data Analysis Exercise (start) • Knowledge Component Model Editing

37. Exploratory Data Analysis Exercise • Goals: 1) Get familiar with data 2) Learn/practice Excel skills • Tasks: 1) create a “step table” 2) graph learning curves

38. TWO_CIRCLES_IN_SQUARE problem: Initial screen

39. TWO_CIRCLES_IN_SQUARE problem: An error a few steps later

40. TWO_CIRCLES_IN_SQUARE problem: Student follows hint & completes prob

41. Exported File Loaded into Excel

42. See handout of exercise …Do some of in next session

43. Overview • DataShop Overview • Logging model • DataShop Features • Quantitative models of learning curves • Power law, logistic regression • Contrasting KC models • Exploratory Data Analysis Exercise (start) • Knowledge Component Model Editing

44. DataShop Demo • Examples of exercise • KC model editing

45. END