Modeling the Macro-Behavior of Learning Object

Repositories

Xavier OchoaEscuela Superior Politécnica del Litoral

http://www.slideshare.net/xaoch

Publishing Learning Objects

• It is a “simple” process:– Upload or point to the material– Fill some metadata– Share!

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

From Micro to Macro

• Studied for other fields– Publication of papers– Application for patents– Economic transactions

Growth in Objects

• Some grow linearly others exponentially

Objects per Contributor

• Heavy-tailed distributions (no bell curve)

LORP - LORFLotka

“fat-tail”

Objects per Contributor

• Heavy-tailed distributions (no bell curve)

OCW - LMSWeibull

“fat-belly”

Objects per Contributor

• Heavy-tailed distributions (no bell curve)

IRExtreme Lotka

“big-head”

Objects per Contributor – Impl.

There is no such thing as an “average user”

Engagement is the key

Enagement is the key

LMSs are the best type of Repository!!!

Modeling LOR

• Publication Rate Distribution (PRD)

• Lifetime Distribution (LTD)

• Contributor Growth Function (CGF)

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

Modeling LOR

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).

Model Validation Parameter Estimation

Model ValidationComparison of results N

Model Validation

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.

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

Conclusions

• Altering the lifetime distribution (that is engagement) change the kind of growth of the repository

Gracias / Obrigado / Thank you

Xavier Ochoaxavier@cti.espol.edu.echttp://ariadne.cti.espol.edu.ec/xavierTwitter: @xaoch