Chapter 30

Handbook of Learning Analytics
First Edition

Linked Data for Learning Analytics:
Potentials and Challenges

Amal Zouaq, Jelena Jovanović, Srećko Joksimović & Dragan Gašević


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.

Export Citation: Plain Text (APA)     BIBTeX     RIS

Supplementary Material
References (42)
About this Chapter
Founding Members
Previous Image
Next Image

info heading

info content