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Learning Analytics: Process & Theory

Learning Analytics: Process & Theory. March 24, 2014. Generalizability. Does your model remain predictive when used in a new data set? Underlies the cross-validation paradigm that is common in data mining

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Learning Analytics: Process & Theory

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  1. Learning Analytics: Process & Theory March 24, 2014

  2. Generalizability • Does your model remain predictive when used in a new data set? • Underlies the cross-validation paradigm that is common in data mining • Knowing the context the model will be used in drives what kinds of generalization you should study

  3. What kind(s) of generalizability • Did Baker et al. 2008 look for?

  4. What kind(s) of generalizability • Did Baker et al. 2013 look for?

  5. What kinds of generalizability • Could one test for?

  6. For each kind of generalizability • Has anyone done it? • What are the advantages to doing it? • What are the practical challenges/barriers/costs?

  7. Given… • Given the massive cost of conducting every possible type of generalizability analysis • How do we know when to stop?

  8. Let’s go back to the class on validity… • Since we didn’t quite finish our discussion…

  9. Types of validity we discussed • Ecological • Construct • Predictive • Substantive • Content • Can anyone define and give a quick example of each?

  10. Exercise • In groups of 3 • Write the abstract of the worst EDM paper ever

  11. Any group want to share?

  12. Exercise #2 • In different groups of 3 • Now write the abstract of the best EDM paper ever

  13. Any group want to share?

  14. The End

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