Chapter 30

Handbook of Learning Analytics
First Edition

Linked Data for Learning Analytics:
Potentials and Challenges

Amal Zouaq, Jelena Jovanović, Srećko Joksimović & Dragan Gašević


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.

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About this Chapter

Title
Linked Data for Learning Analytics: Potentials and Challenges

Book Title
Handbook of Learning Analytics

Pages
pp. 347-355

Copyright
2017

DOI
10.18608/hla17.030

ISBN
978-0-9952408-0-3

Publisher
Society for Learning Analytics Research

Authors
Amal Zouaq1
Jelena Jovanović2
Srećko Joksimović3
Dragan Gašević3,4

Author Affiliations
1. School of Electrical Engineering and Computer Science, University of Ottawa, Canada
2. Department of Software Engineering, University of Belgrade, Serbia
3. Moray House School of Education, University of Edinburgh, United Kingdom
4. School of Informatics, University of Edinburgh, United Kingdom

Editors
Charles Lang5
George Siemens6
Alyssa Wise7
Dragan Gašević3,4

Editor Affiliations
5. Teachers College, Columbia University, USA
6. LINK Research Lab, University of Texas at Arlington, USA
7. Learning Analytics Research Network, New York University, USA

Founding Members
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