1. Core Methods in Educational Data Mining
EDUC545 Spring 2017

2. The Homework
Let’s go over the homework

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

4. 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…

5. 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?

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

7. Questions? Comments? Concerns?

8. Textbook/Readings

9. What is a behavior detector?

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

11. What are some of the methods for collecting ground truth for complex behavior?
What are their advantages and disadvantages?

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

13. What are some indicators of ground truth for student success?
What are their advantages and disadvantages?

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

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

16. Thoughts on the Kai et al. paper?

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

18. 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

19. 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?

20. Other questions, comments, concerns about lectures?

21. Basic HW 3

22. Basic HW 3 Take a couple of models
Apply some standard metrics for them
Available on TutorShop

23. Questions about Basic HW 3?

24. Questions? Concerns?

25. Other questions or comments?

26. Clustering slides 45-51

27. Next Class Feburary 22 Diagnostic Metrics
Baker, R.S. (2015) Big Data and Education. Ch. 2, V1, V2, V3, V4.
Jeni, L. A., Cohn, J. F., & De La Torre, F. (2013). Facing Imbalanced Data--Recommendations for the Use of Performance Metrics. Proceedings of Affective Computing and Intelligent Interaction (ACII),
Knowles, J. E. (2014). Of needles and haystacks: Building an accurate statewide dropout early warning system in Wisconsin. Madison, WI: Wisconsin Department of Public Instruction. 
Basic HW 3 due!

28. The End