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

Danke, questions?

Xavier Ochoa – xavier@cti.espol.edu.ec

Erik Duval – Erik.Duval@cs.kuleuven.be