Quantiative Analysis of Learning Object Repositories
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Quantiative Analysis of Learning Object Repositories Xavier Ochoa, ESPOL, Ecuador Erik Duval, KULeuven, Belgium 2008
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Presentation at ED-Media 2008 Measure several characteristics of Learning Object Repositories: Size, Growth, Contribution Base and Reuse
Transcript of Quantiative Analysis of Learning Object Repositories
Quantiative Analysis of Learning Object Repositories
Xavier Ochoa, ESPOL, Ecuador
Erik Duval, KULeuven, Belgium
2008
Thanks for being here
Slides at...
http://www.slideshare.net/xaoch
Agenda
• What we currently (don’t) know
• Quantitative Studies and Implications– Size– Growth– Contribution– Reuse
• Conclusions
Learning Object Economy
Market Makers
Producers Consumers
Policy Makers
Market
Learning Object Economy
Market Makers
Producers Consumers
Policy Makers
LOR(Market)
Learning Object Economy
Market Makers
Producers Consumers
Policy Makers
LOR(Market)
LOR(Market)
How many objects are published?
How do they grow?
Which percentage is reused?
How much does a user publish?
Does the granularity affect reuse?
Quantitative Analysis
• What we measured (example)– Repositories (ARIADNE)– Referatories (MERLOT)– OpenCourseWare (MIT OCW)– Learning Management Systems (Moodle)– Institutional Repositories (Georgia Tech)
Repository Size
• Power Law – unequal distribution
Repository Size
Repository Referatory OCW LMS IR
Repository Size - Implications
• Interoperability is necessary
• LMS / OCW are as big as LOR(P/F)
• A course uses around 10 to 50 LOs
Growth in Objects
• Growth is Linear (Bi-phase linear)
Growth in Objects - Implications
• Our current strategy is not working!
• All repositories go through 2 phases:– Initial, slow growth (1-3 first years)– Mature, faster growth
• OCW and LMS grow 1 course per day!
Growth in Contributors
• Some are Exponential !
Growth in Contributors – Impl.
• We are not retaining our contributors
• LMS and OCW seem to attract more contributors
• There is a hope!
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
“light-head”
Objects per Contributor – Impl.
There is no such thing as an “average user”
Low
Middle
High
Engagement is the key
Enagement is the key
LMSs are the best type of Repository!!!
LMSs are the best type of Repository!!!
Percentage of Reuse
• 3 LO collections of different granularity:– Components in Slides in ARIADNE– Modules in Connexions– Courses at ESPOL
• Compared with:– Images in Wikipedia articles– Software Libraries in Freshmeat– Web APIs in Mashups
Percentage of Reuse
20% of Learning Objects in a collection
are reused at least once
Percentage of Reuse
20% of Learning Objects in a collection
are reused at least once
NO MATTER THEIR GRANULARITY!
We have to re-think the Reuse Paradox
Reuses per Object
• Log-Normal (also heavy-tailed)
Reuses per Object – Impl.
Reuse seems to be the result of a chain of successful events
Reuse vs. Popularity
What’s Next
• Apply to other Learning Object “Markets”
• Continue analysis of reuse
• Other aspects: creation, updating, use...
Conclusions
• We can gain a lot of knowledge with some simple measurements
• This knowledge benefits – Market Makers– Policy Makers
• We call this “Learnometrics”
• Only way to know if we are moving forward
MESURE(and let us help / let us know)
Real, Real Conclusion
