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Core Methods in Educational Data Mining

Core Methods in Educational Data Mining. EDUC 691 Spring 2019. Assignment C3. Assignment C3. Who here used principal component analysis?. Assignment C3. Who here used principal component analysis? One person come up here and explain your solution How did you do it? What tool did you use?

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Core Methods in Educational Data Mining

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  1. Core Methods in Educational Data Mining EDUC 691Spring 2019

  2. Assignment C3

  3. Assignment C3 • Who here used principal component analysis?

  4. Assignment C3 • Who here used principal component analysis? • One person come up here and explain your solution • How did you do it? • What tool did you use? • What was your final result?

  5. Did anyone else • Use the same method • Get a different answer • What was your answer? • What did you do differently?

  6. Assignment C3 • Who here used a different type of factor analysis? (Not including LFA) • One person come up here and explain your solution • How did you do it? • What tool did you use? • What was your final result?

  7. Did anyone else • Use another factor analysis method • Get a different answer • What was your answer? • What did you do differently?

  8. Assignment C3 • Who here used Learning Factors Analysis? • One person come up here and explain your solution • How did you do it? • What tool did you use? • What was your final result?

  9. Did anyone else • Use the same method • Get a different answer • What was your answer? • What did you do differently?

  10. Assignment C3 • Who here used Barnes’s Q-Matrix method? • One person come up here and explain your solution • How did you do it? • What tool did you use? • What was your final result?

  11. Did anyone else • Use the same method • Get a different answer • What was your answer? • What did you do differently?

  12. Assignment C3 • Who here used Knowledge Spaces or Partial Order Knowledge Spaces? • One person come up here and explain your solution • How did you do it? • What tool did you use? • What was your final result?

  13. Did anyone else • Use the same method • Get a different answer • What was your answer? • What did you do differently?

  14. Assignment C3 • Who here used Non-Negative Matrix Factorization?

  15. Assignment C3 • Who here used Non-Negative Matrix Factorization? • One person come up here and explain your solution • How did you do it? • What tool did you use? • What was your final result?

  16. Assignment C3 • Did anyone use a different method than any of these? • What did you use? • How did it work?

  17. The true answer…

  18. Why… • Did folks use the methods they used? • Did folks not use the other methods?

  19. Easy to use tools matter, right?

  20. What are… • The pluses and minuses of PCA for this problem?

  21. What are… • The pluses and minuses of other FA for this problem?

  22. What are… • The pluses and minuses of LFA for this problem?

  23. What are… • The pluses and minuses of Barnes’s method for this problem?

  24. What are… • The pluses and minuses of KS/POKS for this problem?

  25. What are… • The pluses and minuses of NNMF for this problem?

  26. Any other questions or comments on the assignment?

  27. What could I have changed about the assignment… • That would have made LFA the obviously best approach to solving it?

  28. What could I have changed about the assignment… • That would have made KS/POKS the obviously best approach to solving it?

  29. What are the relative benefits of using a q-matrix versus a knowledge space?

  30. What are the consequences of getting a knowledge mapping wrong?

  31. What are the relative advantages of • Automatic model discovery • Hand-development and refinement • Hybrid approaches

  32. What does a spike in a learning curve mean? (Distinct from MBMLM)

  33. What is the trade-off • Between Knowledge Spaces • And Bayes Nets • ?

  34. Other questions or comments?

  35. Next Class • Wednesday, April 24 • Assignment B7 due • No office hours next week! • Baker, R.S. (2015) Big Data and Education. Ch. 5, V1, V2. • Rai, D., Beck, J.E. (2011) Exploring user data from a game-like math tutor: a case study in causal modeling. Proceedings of the 4th International Conference on Educational Data Mining, 307-313. • Rau, M. A., Scheines, R. (2012) Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations. Proceedings of the 5th International Conference on Educational Data Mining, 110-117.  • Slater, S., Ocumpaugh, J., Baker, R., Scupelli, P., Inventado, P.S., Heffernan, N. (2016) Semantic Features of Math Problems: Relationships to Student Learning and Engagement. Proceedings of the 9th International Conference on Educational Data Mining, 223-230. • Guest lecturer: Stefan Slater

  36. The End

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