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Towards a better understanding of learning objects' content

  • Ramzi Farhat
  • , Bruno Defude
  • , Mohamed Jemni

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Usually semantic metadata are introduced to support effective search of relevant learning objects from LOR (Learning Object Repositories). We propose to use them to assist authors of SCORM (Sharable Content Object Reference Model) like learning objects. In fact, when we design new learning objects by reuse of existing ones, there are risks to have weaknesses mainly due to a bad understanding of the true nature of the reused objects. To overcome those risks we have defined an automated authoring assistance approachl. In this paper we focus especially on how we use semantic metadata to produce indicators about the content of learning objects, more precisely about their complexity, heterogeneity and imbalance from a semantic perspective. To attend this specific objective we introduce the notion of "semantic space" and "semantic component". Then we use them to compute metrics which are aggregated together to produce meaningful indicators.

Original languageEnglish
Title of host publicationProceedings of the 2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT 2011
Pages536-540
Number of pages5
DOIs
Publication statusPublished - 19 Sept 2011
Externally publishedYes
Event2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT 2011 - Athens, GA, United States
Duration: 6 Jul 20118 Jul 2011

Publication series

NameProceedings of the 2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT 2011

Conference

Conference2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT 2011
Country/TerritoryUnited States
CityAthens, GA
Period6/07/118/07/11

Keywords

  • Author assistance
  • Content indicators
  • Semantic metadata

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