Semantic gap detection in metadata of adaptive learning environments

  • Sergey Sosnovsky
  • , Isaac Alpizar Chacon

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

1 Scopus citations

Abstract

Quality of learning objects metadata, in many respects, defines the quality of an adaptive learning environment presenting these learning objects to a student. Metadata inconsistencies and gaps may be the cause of various problems: from a system malfunction to ineffective learning experiences. In this paper, we propose an intelligent and rigorous mechanism for detecting metadata gaps in collections of learning content. The mechanism converts learning objects metadata into an OWL2 ontology, detects logical conflicts using Semantic Web reasoning techniques and generates human-readable explanations for an author to resolve the gaps. The evaluation of the developed semantic gap detection tool with real learning content collections demonstrates its effectiveness.

Original languageEnglish
Title of host publicationProceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014
EditorsDemetrios G. Sampson, Michael J. Spector, Nian-Shing Chen, Ronghuai Huang, Kinshuk
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages548-552
Number of pages5
ISBN (Electronic)9781479940387
DOIs
StatePublished - 17 Sep 2014
Event14th IEEE International Conference on Advanced Learning Technologies, ICALT 2014 - Athens, Greece
Duration: 7 Jul 20149 Jul 2014

Publication series

NameProceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014

Conference

Conference14th IEEE International Conference on Advanced Learning Technologies, ICALT 2014
Country/TerritoryGreece
CityAthens
Period7/07/149/07/14

Keywords

  • Adaptive learning environment
  • authoring support
  • learning object metadata
  • metadata gap
  • semantic reasoning

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