Populating learning object repositories with hidden internal quality information

Cristian Cechinel, Sandro Silva Da Camargo, Xavier Ochoa, Salvador Sánchez Alonso, Miguel Ángel Sicilia

Research output: Contribution to journalConference articlepeer-review


It is known that current Learning Object Repositories adopt strategies for quality assessment of their resources that rely on the impressions of quality given by the members of the repository community. Although this strategy can be considered effective at some extent, the number of resources inside repositories tends to increase more rapidly than the number of evaluations given by this community, thus leaving several resources of the repository without any quality assessment. The present work describes the results of an experiment for automatically generate quality information about learning resources inside repositories through the use of Artificial Neural Networks models. We were able to generate models for classifying resources between good and not-good with accuracies that vary from 50% to 80% depending on the given subset. The preliminary results found here point out the feasibility of such approach and can be used as a starting point for the pursuit of automatically generation of internal quality information about resources inside repositories.

Original languageEnglish (US)
Pages (from-to)11-22
Number of pages12
JournalCEUR Workshop Proceedings
StatePublished - 2012
Event2nd Workshop on Recommender Systems in Technology Enhanced Learning 2012, RecSysTEL 2012 - In Conjunction with the 7th European Conference on Technology Enhanced Learning, EC-TEL 2012 - Saarbrucken, Germany
Duration: Sep 18 2012Sep 19 2012


  • Artificial neural networks
  • Learning object repositories
  • Learning objects
  • Ranking mechanisms
  • Ratings

ASJC Scopus subject areas

  • General Computer Science


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