Learnometrics Keynote LAK2011

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Keynote at Learning Analytics and Knowledge 2011

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Learnometrics Keynote LAK2011

  1. 1. Learnometrics: Metrics for Learning (Objects) Xavier Ochoa Escuela Superior Politécnica del Litoral Guayaquil, Ecuador
  2. 2. http://www.slideshare.net/xaoch
  3. 5. Research about Learning Objects But what?
  4. 6. What is a Learning Object? Not a good idea
  5. 7. What is a Learning Object? Not a good idea
  6. 8. How many learning objects are published? How many learning objects are reused? How many objects are produced by each teacher? How many times a learning object is reused? What is the average size of a LO repository?
  7. 10. Learning Object Repositories
  8. 11. Size of Repositories Repository Referatory OCW LMS IR
  9. 12. A medium sized LMS has more objects than MERLOT
  10. 13. Distribution of Objects
  11. 14. There is a long tail of resources How can we find them?
  12. 15. Growth – in Objects
  13. 16. Growth in Contributors
  14. 17. Growth is Linear Ouch!
  15. 18. But there is hope…
  16. 19. Connexions Growth in Contributors
  17. 20. Connexions Growth in Objects
  18. 22. Objects per Contributor LORP - LORF Lotka / Log-Normal “ fat-tail”
  19. 23. Objects per Contributor OCW - LMS Weibull “ fat-belly”
  20. 24. Objects per Contributor IR Lotka high alpha “ light-tail”
  21. 25. The key is the engagement There must be a value proposition
  22. 26. Engagement
  23. 27. If only our LMSs could be our repositories OERs could be the key
  24. 28. Reuse of LO
  25. 29. Reuse is the raison d’être of Learning Objects But very little is know about actual reuse
  26. 30. Reuse Paradox
  27. 31. Measuring Reuse
  28. 32. Measuring Reuse
  29. 33. Measuring Reuse ~20%
  30. 35. REUSE IS HAPENNING with or without us Let’s help to make it easier
  31. 36. Our understanding of Re-use needs to be re-examined We need more studies!
  32. 37. Popularity vs. Reuse
  33. 38. Distribution of Reuse
  34. 39. We call this line of research Learnometrics Hey, it was good for my thesis 
  35. 40. Learnometrics <ul><li>Study empirical regularities on data </li></ul><ul><li>Develop mathematical models </li></ul><ul><li>To understand the influence/impact </li></ul><ul><li>Produce useful metrics </li></ul>
  36. 41. Learnometrics <ul><li>Study empirical regularities on data </li></ul><ul><li>Develop mathematical models </li></ul><ul><li>To understand the influence/impact </li></ul><ul><li>Produce useful metrics </li></ul>
  37. 42. Learning Analytics <ul><li>Study empirical regularities on data </li></ul><ul><li>Develop mathematical models </li></ul><ul><li>To understand the influence/impact </li></ul><ul><li>Produce useful metrics </li></ul>
  38. 43. Educational Data Mining? <ul><li>Study empirical regularities on data </li></ul><ul><li>Develop mathematical models </li></ul><ul><li>To understand the influence/impact </li></ul><ul><li>Produce useful metrics </li></ul>
  39. 44. Learngin Analytics or Educational Data Mining or Educational Research The same or different?
  40. 46. The questions are the same The difference is in the kind of answers
  41. 47. Gracias / Thank you / Merci Xavier Ochoa [email_address] http://ariadne.cti.espol.edu.ec/xavier Twitter: @xaoch

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