1. Learning in MOOCs! Evidence and Correlates Dave Pritchard and //RELATE.MIT.edu S. Rayyan, R. Teodorescu, A. Pawl, Y. Bergner, A. Barrantes, Chen, D. Seaton, C. Fredericks, J. Champaign, K. Colvin, A. Liu, J. Doucette Evidence of Learning/Improved Learning? What Activities Correlate with Learning? What Behaviors Correlate with Learning? Two MOOCs: our 8.MReV – Mechanics Review 6.002x – MIT Electronics and Circuits

2. Simple Way to Measure Learning ? 8MReV only Give Same Test pre- and post- instruction See if there is Improvement, Gain = (post-pre)

3. Gain and Normalized Gain (-slope) Normalized Gain g = 0.40 Forbidden Region: More than 100% on posttest! 0% 100% –100% Gain (= Post – Pre)  Learn Everything g = 1.00 Pretest Percentage  100% 0 % Normalized Gain g = Gain (= Post – Pre) 100% - Pre g is the fraction of unknowns on pretest learned on post test

4. Gain (posttest – pretest) vs Pretest From R. Hake’s study of 6545 students in 62 classes. HS  Top College

5. Force Questions Gain in 8.MReV MOOC g = 0.30± 0.02 6 Items Gain vs Pre-Score: equal Learning for all cohorts

6. Non-Force Concept Questions 8.MReV g = 0.33 ± 0.02 5 Items N = 343

7. Concept and Quantitative 8.MReV g = 0.41± 0.03 7 Items (2 quantitative) N = 176

8. What & Why Item Response Theory Measures ability or skill of student – Independent of which Questions Answered – Intrinsic, not extrinsic (like total score) Sophisticated grading on a curve – In Standard Deviations from Class Average We use it Two Ways: – Alternate way to analyze pre and post-test – Measure Relative Improvement HW and Tests

9. Average Skill in Course  -2.0 -1.0 0.01.0 2.0 Skill Increase in Course  IRT Skill Increase Pre  Post N =579 The key finding here is that the less skillful students learn as much as more skillful students

10. Summary – Conceptual Learning Conceptual Learning in 8.MReV slightly greater than traditional on-campus course None of the various cohorts we studied showed significantly less normalized gain – HS students vs those with advanced degrees – poor prerequisites: math or physics courses – Students of low average skill Contrary to concerns, no evidence that unskillful, less educated, or less prepared students learn less

11. Teachers, Non-Teachers, and MIT Students We use 253 questions in both 8.011 and MOOC

12. Weekly IRT Skill of 8.MReV Various Cohorts versus on-campus students On-campus students have the advantage of a flipped classroom with MAPS instruction Hypothesis: They should show steady improvement relative to MOOC students

13. There is no significant relative improvement of the 8.011 students. On-Campus vs 8.MReV Weekly Skills -Does Class Improve Skill?

14. Relative Improvement 0.6 (Skill Average -0.50 )

15. 8.MReV Where Students Spent Time Students attempting more than 50% of problems (N=1080). Note that cool colors indicate instruction and warm colors indicate assessment

16. What Correlates with Learning? Time on Task? Initial Knowledge? Study Habits?

17. The fractional division of time among the various resources of 6.002x Data are for XXXX certificate earners who spent an average of 95 hours on the entire course. Note that cool colors indicate instruction and warm colors indicate assessment

18. Correlates of Weekly Improvement and Gain Based on weekly IRT skills (e.g. on a curve) Find the slope of these: Relative Improvement Correlate with time on various components – eText, Video, Discussion (instructional) – Checkpoint questions, Homework (assessment)

19. Correlation Coefficients Visualized -0.62 +0.30 Color  Sign Fraction  Number

20. 8.MReV Where Students Spent Time Students attempting more than 50% of problems (N=1080). Note that cool colors indicate instruction and warm colors indicate assessment

21. Checkpoint Discussion eText Problems Total Time Posttest-Pretest Gain Average Skill Initial Skill Relative Improvement “Score” in Course 8.MReV Measures of Skills and Log of Time on Tasks (N = 292)

22. The fractional division of time among the various resources of 6.002x Data are for XXXX certificate earners who spent an average of 95 hours on the entire course. Note that cool colors indicate instruction and warm colors indicate assessment

23. Homework Video Lab Book Tutorial Discussion Wiki Total Time Skill Avg Skill Initial Relative Improve Score Lecture Questions 6.002x Measures of Skills and Log of Time on Tasks (n=5948) Do students who spend more time watching lecture videos improve more? No, they improve less Do students who spend more time on Homework have higher skill? No, negative correlation

24. Why Negative correlations!? More time on HW or Labs  more skill? More skill  takes less time to do HW or Lab! Why do we suppose the same instruction will benefit students widely different in skill? Maybe we can analyze particular cohorts to find effective instruction for some!

25. Conclusions and Future 8.MReV – Positive correlations with conceptual learning – Weaker correlation with Relative Improvement 6.002x: Broad Range of Skills & Demographics – Strong Negative Correlations with Skills – No significant Correlation with Relative Improve’t Future: – examine different cohorts – Experimental/Control group experiments – Student Habits & Clusters of Characteristics

26. Predicting (Classifying) Improvement 8.MReV AlgorithmAccuracy % Support Vector Machcine55 +/- 1 Decision Tree Learner C4.5 J48 Weka71 +/- 6 Multiple Regression73 +/- 6 We used various Machine Learning Algorithms to predict whether students would be above or below average in relative improvement. (50% correct is pure guessing)

27. Your Measurement Affects the Result Like Quantum Mechanics, only worse

28. Palazzo, D. et. al. Phys. Rev. ST Phys. Educ. Res. vol. 6, (2010), p. 010104 Symbolic answer: 2.4 Sigma Learning! But no help on conceptual Amount of Symbolic Homework Copied  Closer Look At Homework Copying

29. Symbolic vs. Conceptual Difference! ?? Physics Teacher Expectation Students Start Symbolic Problems from Conceptual Analysis Answer Numerical Questions by Plugging in Symbolic answer The problems cover the same topics, so This result is Unexpected  Students not Experts

30. Palazzo, D. et. al. Phys. Rev. ST Phys. Educ. Res. vol. 6, (2010), p. 010104 Homework Copying Amount of Symbolic Homework Copied  Symbolic answer: 2.4 Sigma Learning! But no help on conceptual

31. LORE: Library of Open Research-based Educational Resources National Research Council: “research- based educational resources produce dramatically better learning outcomes” Open edX.org MOOC platform – Have content from ~50 universities & organizations – Rapid way to vet assessments – Enables big-data analysis of learning

32. The LORE Library Catalog with informative and actionable metadata – Learning Objective – Level & Difficulty, Time to Complete… Directly assignable and automatically graded Vetted by trusted process

33. Library of Research-Based Resources Vetted Calibrated Library Testing New Teacher MOOC Teacher MOOC control attend control Course builder Student MOOC Student MOOC Data Mining Psychometrics Data Mining Psychometrics control attend

34. 34 Classical Test vs. IRT – MIT data Classical Test Item Response Theory The IRT graph has less error and shows the trend better: Students selected by SAT scores have an advantage until the fifth week of the course at MIT (vs. second semester in most colleges as claimed by ETS). Chapter  Fraction Correct  Classical Test Theory Item Response Theory MIT 8.01 Class MasteringPhysics Chapter  Chapter Std. Dev. Above