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Modeling the Macro-Behavior of Learning Object Repositories


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Presentation at LACLO 2010. How the publication in Learning Object Repositories can be simply modelled based on the rate of production, the lifetime and the user growth.

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Modeling the Macro-Behavior of Learning Object Repositories

  1. 1. Modeling the Macro-Behavior of Learning Object Repositories<br />Xavier Ochoa<br />Escuela Superior Politécnica del Litoral<br />
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  3. 3. Publishing Learning Objects<br />It is a “simple” process:<br />Upload or point to the material<br />Fill some metadata<br />Share!<br />
  4. 4. Publishing Learning Objects<br />This simple process determines the micro-behavior of contributors and consumers<br />This give rise to complex macro-behavior at the repository level once hundreds or thousands of individuals are aggregated<br />
  5. 5. From Micro to Macro<br />Studied for other fields<br />Publication of papers<br />Application for patents<br />Economic transactions<br />
  6. 6. Growth in Objects<br />Some grow linearly others exponentially<br />
  7. 7. Objects per Contributor<br />Heavy-tailed distributions (no bell curve)<br />LORP - LORF<br />Lotka <br />“fat-tail”<br />
  8. 8. Objects per Contributor<br />Heavy-tailed distributions (no bell curve)<br />OCW - LMS<br />Weibull <br />“fat-belly”<br />
  9. 9. Objects per Contributor<br />Heavy-tailed distributions (no bell curve)<br />IR<br />Extreme Lotka<br />“big-head”<br />
  10. 10. Objects per Contributor – Impl.<br />There is no such thing as an “average user”<br />
  11. 11. Engagement is the key<br />
  12. 12. Enagement is the key<br />LMSs are the best type <br />of Repository!!!<br />
  13. 13. Modeling LOR<br />Publication Rate Distribution (PRD)<br />Lifetime Distribution (LTD)<br />Contributor Growth Function (CGF)<br />
  14. 14. Modeling LOR<br />The period of time, measured in days is selected.<br />The Contributor Growth Function (CGF) is used to calculate the size of the contributor population <br />A virtual population of contributors of the calculated size is created.<br />For each contributor: <br />the two basic characteristics, publication rate and lifetime are assigned (PRD) and (LTD)<br />Each contributor is assigned a starting date (CGF). <br />The simulation is run<br />
  15. 15. Modeling LOR<br />
  16. 16. Model Validation<br />To validate this model we compare the simulated results against the data extracted from real repositories. <br />Three characteristics of the repository are compared: <br />distribution of the number of publications among contributors (N)<br />the shape of the content growth function (GF)<br />the final size of the repository (S).<br />
  17. 17. Model Validation Parameter Estimation<br />
  18. 18. Model ValidationComparison of results N<br />
  19. 19. Model Validation<br />
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  24. 24. Conclusions<br />Simple assumptions:<br />how frequently the contributors publish material (publication rate)<br />how much time they persist in their publication efforts (lifetime)<br />at which rate they arrive at the repository (contributor growth function). <br />Predict:<br />distribution of publications among contributors<br />the shape of the content growth function<br />final size of the repository.<br />
  25. 25. Conclusions<br />Simple model that presents errors… but it is TESTABLE<br />New models can be constructed and tested to determine if they are better or worst<br />Give a way to measure the goodness of the ideas<br />
  26. 26. Conclusions<br />Altering the lifetime distribution (that is engagement) change the kind of growth of the repository<br />
  27. 27. Gracias / Obrigado / Thank you<br />Xavier Ochoa<br /><br /><br />Twitter: @xaoch<br />