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.

1. Modeling the Macro-Behavior of Learning Object Repositories Xavier Ochoa Escuela Superior Politécnica del Litoral

2. http://www.slideshare.net/xaoch

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