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Quantiative Analysis of Learning Object Repositories Xavier Ochoa, ESPOL, Ecuador Erik Duval, KULeuven, Belgium 2008
Thanks for being here
Slides at... <ul><li>http://www.slideshare.net/xaoch </li></ul>
Agenda <ul><li>What we currently (don’t) know </li></ul><ul><li>Quantitative Studies and Implications </li></ul><ul><ul><l...
Learning Object Economy Market Makers Producers Consumers Policy Makers Market
Learning Object Economy Market Makers Producers Consumers Policy Makers LOR (Market)
Learning Object Economy Market Makers Producers Consumers Policy Makers LOR (Market)
How many objects are published? How do they grow? Which percentage is reused? How much does a user publish? Does the granu...
Quantitative Analysis <ul><li>What we measured (example) </li></ul><ul><ul><li>Repositories (ARIADNE) </li></ul></ul><ul><...
Repository Size <ul><li>Power Law – unequal distribution </li></ul>
Repository Size Repository Referatory OCW LMS IR
Repository Size - Implications <ul><li>Interoperability is necessary </li></ul><ul><li>LMS / OCW are as big as LOR(P/F) </...
Growth in Objects <ul><li>Growth is Linear    (Bi-phase linear) </li></ul>
Growth in Objects - Implications <ul><li>Our current strategy is not working! </li></ul><ul><li>All repositories go throug...
Growth in Contributors <ul><li>Some are Exponential   ! </li></ul>
Growth in Contributors – Impl. <ul><li>We are not retaining our contributors </li></ul><ul><li>LMS and OCW seem to attract...
Objects per Contributor <ul><li>Heavy-tailed distributions (no bell curve) </li></ul>LORP - LORF Lotka  “ fat-tail”
Objects per Contributor <ul><li>Heavy-tailed distributions (no bell curve) </li></ul>OCW - LMS Weibull  “ fat-belly”
Objects per Contributor <ul><li>Heavy-tailed distributions (no bell curve) </li></ul>IR Extreme Lotka “ light-head”
Objects per Contributor – Impl. There is no such thing as an “average user ”
Low Middle High
Engagement is the key
Enagement is the key LMSs are the best type  of Repository!!!
Percentage of Reuse <ul><li>3 LO collections of different granularity: </li></ul><ul><ul><li>Components in Slides in ARIAD...
Percentage of Reuse <ul><li>20% of Learning Objects in a collection </li></ul><ul><li>are reused at least once </li></ul>
Percentage of Reuse <ul><li>20% of Learning Objects in a collection </li></ul><ul><li>are reused at least once </li></ul><...
Reuses per Object <ul><li>Log-Normal (also heavy-tailed) </li></ul>
Reuses per Object – Impl. <ul><li>Reuse seems to be the result of a chain of successful events </li></ul>
Reuse vs. Popularity
What’s Next <ul><li>Apply to other Learning Object “Markets” </li></ul><ul><li>Continue analysis of reuse </li></ul><ul><l...
Conclusions <ul><li>We can gain a lot of knowledge with some simple measurements </li></ul><ul><li>This knowledge benefits...
MESURE (and let us help / let us know) Real, Real Conclusion
Danke, questions? Xavier Ochoa  –  [email_address] Erik Duval  –  [email_address]
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Quantiative Analysis of Learning Object Repositories

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Presentation at ED-Media 2008
Measure several characteristics of Learning Object Repositories: Size, Growth, Contribution Base and Reuse

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Transcript of "Quantiative Analysis of Learning Object Repositories"

  1. 1. Quantiative Analysis of Learning Object Repositories Xavier Ochoa, ESPOL, Ecuador Erik Duval, KULeuven, Belgium 2008
  2. 2. Thanks for being here
  3. 3. Slides at... <ul><li>http://www.slideshare.net/xaoch </li></ul>
  4. 4. Agenda <ul><li>What we currently (don’t) know </li></ul><ul><li>Quantitative Studies and Implications </li></ul><ul><ul><li>Size </li></ul></ul><ul><ul><li>Growth </li></ul></ul><ul><ul><li>Contribution </li></ul></ul><ul><ul><li>Reuse </li></ul></ul><ul><li>Conclusions </li></ul>
  5. 5. Learning Object Economy Market Makers Producers Consumers Policy Makers Market
  6. 6. Learning Object Economy Market Makers Producers Consumers Policy Makers LOR (Market)
  7. 7. Learning Object Economy Market Makers Producers Consumers Policy Makers LOR (Market)
  8. 8. How many objects are published? How do they grow? Which percentage is reused? How much does a user publish? Does the granularity affect reuse?
  9. 9. Quantitative Analysis <ul><li>What we measured (example) </li></ul><ul><ul><li>Repositories (ARIADNE) </li></ul></ul><ul><ul><li>Referatories (MERLOT) </li></ul></ul><ul><ul><li>OpenCourseWare (MIT OCW) </li></ul></ul><ul><ul><li>Learning Management Systems (Moodle) </li></ul></ul><ul><ul><li>Institutional Repositories (Georgia Tech) </li></ul></ul>
  10. 10. Repository Size <ul><li>Power Law – unequal distribution </li></ul>
  11. 11. Repository Size Repository Referatory OCW LMS IR
  12. 12. Repository Size - Implications <ul><li>Interoperability is necessary </li></ul><ul><li>LMS / OCW are as big as LOR(P/F) </li></ul><ul><li>A course uses around 10 to 50 LOs </li></ul>
  13. 13. Growth in Objects <ul><li>Growth is Linear  (Bi-phase linear) </li></ul>
  14. 14. Growth in Objects - Implications <ul><li>Our current strategy is not working! </li></ul><ul><li>All repositories go through 2 phases: </li></ul><ul><ul><li>Initial, slow growth (1-3 first years) </li></ul></ul><ul><ul><li>Mature, faster growth </li></ul></ul><ul><li>OCW and LMS grow 1 course per day! </li></ul>
  15. 15. Growth in Contributors <ul><li>Some are Exponential  ! </li></ul>
  16. 16. Growth in Contributors – Impl. <ul><li>We are not retaining our contributors </li></ul><ul><li>LMS and OCW seem to attract more contributors </li></ul><ul><li>There is a hope! </li></ul>
  17. 17. Objects per Contributor <ul><li>Heavy-tailed distributions (no bell curve) </li></ul>LORP - LORF Lotka “ fat-tail”
  18. 18. Objects per Contributor <ul><li>Heavy-tailed distributions (no bell curve) </li></ul>OCW - LMS Weibull “ fat-belly”
  19. 19. Objects per Contributor <ul><li>Heavy-tailed distributions (no bell curve) </li></ul>IR Extreme Lotka “ light-head”
  20. 20. Objects per Contributor – Impl. There is no such thing as an “average user ”
  21. 21. Low Middle High
  22. 22. Engagement is the key
  23. 23. Enagement is the key LMSs are the best type of Repository!!!
  24. 24. Percentage of Reuse <ul><li>3 LO collections of different granularity: </li></ul><ul><ul><li>Components in Slides in ARIADNE </li></ul></ul><ul><ul><li>Modules in Connexions </li></ul></ul><ul><ul><li>Courses at ESPOL </li></ul></ul><ul><li>Compared with: </li></ul><ul><ul><li>Images in Wikipedia articles </li></ul></ul><ul><ul><li>Software Libraries in Freshmeat </li></ul></ul><ul><ul><li>Web APIs in Mashups </li></ul></ul>
  25. 25. Percentage of Reuse <ul><li>20% of Learning Objects in a collection </li></ul><ul><li>are reused at least once </li></ul>
  26. 26. Percentage of Reuse <ul><li>20% of Learning Objects in a collection </li></ul><ul><li>are reused at least once </li></ul><ul><li>NO MATTER THEIR GRANULARITY! </li></ul><ul><li>We have to re-think the Reuse Paradox </li></ul>
  27. 27. Reuses per Object <ul><li>Log-Normal (also heavy-tailed) </li></ul>
  28. 28. Reuses per Object – Impl. <ul><li>Reuse seems to be the result of a chain of successful events </li></ul>
  29. 29. Reuse vs. Popularity
  30. 30. What’s Next <ul><li>Apply to other Learning Object “Markets” </li></ul><ul><li>Continue analysis of reuse </li></ul><ul><li>Other aspects: creation, updating, use... </li></ul>
  31. 31. Conclusions <ul><li>We can gain a lot of knowledge with some simple measurements </li></ul><ul><li>This knowledge benefits </li></ul><ul><ul><li>Market Makers </li></ul></ul><ul><ul><li>Policy Makers </li></ul></ul><ul><li>We call this “Learnometrics” </li></ul><ul><li>Only way to know if we are moving forward </li></ul>
  32. 32. MESURE (and let us help / let us know) Real, Real Conclusion
  33. 33. Danke, questions? Xavier Ochoa – [email_address] Erik Duval – [email_address]
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