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Core Methods in Educational Data Mining

Core Methods in Educational Data Mining. HUDK4050 Fall 2014. Assignment B5. Connections. I got a lot of questions on the homework, when 12345 posts 12 times in response to 24601 Is this 12 connections?. Connections.

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Core Methods in Educational Data Mining

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  1. Core Methods in Educational Data Mining HUDK4050Fall 2014

  2. Assignment B5

  3. Connections • I got a lot of questions on the homework, when 12345 posts 12 times in response to 24601 • Is this 12 connections?

  4. Connections • I got a lot of questions on the homework, when 12345 posts 12 times in response to 24601 • Is this 12 connections? • In Social Network Theory, it isn’t • It’s a connection with a strength of 12

  5. What are other ways connection strength could be conceptualized, in an online course?

  6. In the text, I described several types of nodes • In a graph of classroom interactions, there could be several different types of nodes • Teacher • TA • Student • Project Leader • Project Scribe

  7. What could the node types be, in an online course?

  8. In the text, I described several types of links • In a graph of classroom interactions, there could be several types of links • Leadership role (X leads Y) • Working on same learning resource • Helping act • Criticism act • Insult • Note that links can be directed or undirected

  9. What could the link types be, in an online course?

  10. Four types of centrality Degree centrality Closeness centrality Betweeness centrality Eigenvector centrality

  11. What does each one mean mathematically? Degree centrality Closeness centrality Betweeness centrality Eigenvector centrality

  12. What does each one mean conceptually? Degree centrality Closeness centrality Betweeness centrality Eigenvector centrality

  13. Questions or comments?

  14. Network Analysis • The most common use of network analysis is for social networks • When else might it apply?

  15. Network Analysis • Let’s make a list of networks we could model, beyond online course forums

  16. Let’s break into groups • Each group takes a type of network • One group per network type • Define what the meaning in this network would be (or could be) for • Node types, links, link types, link strength • Density • Reachability, Geodesic Distance, Flow • Centrality (in its four forms) • Other measures that might be particularly relevant for your type of network

  17. Now reconvene • Who wants to share • What their network type is • Node types, links, link types, link strength • Anything else that’s interesting

  18. Other questions or comments?

  19. An Example of Discovery with Models with Social Networks Let’s watch together https://www.youtube.com/watch?v=F2y-GFzhEqc

  20. Questions? Comments?

  21. Assignment B6 • Correlation Mining

  22. Next Class • No class Monday

  23. Next Class • Wednesday, November 12 • Baker, R.S. (2014) Big Data and Education. Ch. 5, V1, V2. • Arroyo, I., Woolf, B. (2005) Inferring learning and attitudes from a Bayesian Network of log file data. Proceedings of the 12th International Conference on Artificial Intelligence in Education, 33-40. • 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.

  24. The End

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