Modeling the Macro-Behavior of Learning Object
Repositories
Xavier OchoaEscuela Superior Politécnica del Litoral
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 [email protected]://ariadne.cti.espol.edu.ec/xavierTwitter: @xaoch
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