1. Core Methods in Educational Data Mining HUDK4050 Fall 2014

2. The Homework Let’s go over the homework

3. Was it harder or easier than basic homework 1?

4. Let’s go over some of the solutions you handed in…. I will call on a small number of you

5. Let’s go over some of the solutions you handed in…. If I call on you, please come up and discuss – What you did If you’re not the first person I call, please focus on how your solution differed from previous students – How well it worked If you’re in the audience, please ask questions – But be nice…

6. Anyone else? Does anyone else in the audience have – Something clever they did and want to share? – Something clever they didn’t do but want to discuss? – A concern about how to do this right?

7. What mattered? What could you do to get better model performance? (Without cheating)

8. Questions? Comments? Concerns?

9. Textbook/Readings

10. What is a behavior detector?

11. What are some of the methods for collecting ground truth for complex behavior?

12. What are their advantages and disadvantages?

13. What are some indicators of ground truth for student success?

14. What are their advantages and disadvantages?

15. Thoughts on the San Pedro et al. case study?

16. Thoughts on the Sao Pedro et al. paper?

17. Thoughts on the Baker et al. paper?

18. Grain-sizes Which grain-size(s) were the detection focus for each paper/case study?

19. Grain-sizes What are the advantages and disadvantages of working at these different grain-size(s)? – Student-level – Action-level – Observation-level – Problem/Activity-level – Day/Session-level – Lesson-level

20. Why… Should we not expect (or want) Detectors with Kappa = 0.75 For models built with training labels with inter-rater reliability Kappa = 0.62?

21. Other questions, comments, concerns about lectures?

22. Basic HW 2

23. Take a couple of models Apply some standard metrics for them

24. Questions about Basic HW 2?

25. Questions? Concerns?

26. Other questions or comments?

27. Next Class Wednesday, September 24 Diagnostic Metrics Baker, R.S. (2014) Big Data and Education. Ch. 2, V1, V2, V3, V4. Fogarty, J., Baker, R., Hudson, S. (2005) Case Studies in the use of ROC Curve Analysis for Sensor-Based Estimates in Human Computer Interaction. Proceedings of Graphics Interface (GI 2005), 129-136. Russell, S., Norvig, P. (2010) Artificial Intelligence: A Modern Approach. Ch. 20: Learning Probabilistic Models. Basic HW 2 due

28. Special Session Friday

29. The End