Linked data for learning analytics: potentials and challenges

Amal Zouaq, Jelena Jovanovic, Srécko Joksimovíc, Dragan Gašević

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

Abstract

Learning analytics (LA) is witnessing an explosion of data generation due to the multiplicity and diversity of learning environments, the emergence of scalable learning models such as massive open online courses (MOOCs), and the integration of social media platforms in the learning process. This diversity poses multiple challenges related to the interoperability of learning platforms, the integration of heterogeneous data from multiple knowledge sources, and the content analysis of learning resources and learning traces. This chapter discusses the use of linked data (LD) as a potential framework for data integration and analysis. It provides a literature review of LD initiatives in LA and educational data mining (EDM) and discusses some of the potentials and challenges related to the exploitation of LD in these fields.
Original languageEnglish
Title of host publicationHandbook of Learning Analytics
EditorsCharles Lang, George Siemens, Alyssa Wise, Dragan Gašević
Place of PublicationUSA
PublisherSociety for Learning Analytics Research
Chapter30
Pages347-355
Number of pages9
Edition1st
ISBN (Print)9780995240803
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Linked data (LD)
  • Data integration
  • Content analysis
  • Educational data mining (EDM)

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