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