Quantitative analysis of learning object repositories

Xavier Ochoa, Erik Duval

Research output: Contribution to journalArticle

Abstract

This paper conducts the first detailed quantitative study of the process of publication of learning objects in repositories. This process has been often discussed theoretically, but never empirically evaluated. Several question related to basic characteristics of the publication process are raised at the beginning of the paper and answered through quantitative analysis. To provide a wide view of the publication process, this paper analyzes four types of repositories: Learning Object Repositories, Learning Object Referatories, Open Courseware Initiatives, and Learning Management Systems. For comparison, Institutional Repositories are also analyzed. Three repository characteristics are measured: size, growth, and contributor base. The main findings are that the amount of learning objects is distributed among repositories according to a power law, the repositories mostly grow linearly, and the amount of learning objects published by each contributor follows heavy-tailed distributions. The paper finally discusses the implications that this findings could have in the design and operation of Learning Object Repositories.

Original languageEnglish (US)
Pages (from-to)226-238
Number of pages13
JournalIEEE Transactions on Learning Technologies
Volume2
Issue number3
DOIs
StatePublished - Oct 7 2009

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Keywords

  • Learning objects
  • LMS
  • LOR
  • OCW
  • Publication
  • Repositories

ASJC Scopus subject areas

  • Education
  • Engineering(all)
  • Computer Science Applications

Cite this

Quantitative analysis of learning object repositories. / Ochoa, Xavier; Duval, Erik.

In: IEEE Transactions on Learning Technologies, Vol. 2, No. 3, 07.10.2009, p. 226-238.

Research output: Contribution to journalArticle

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