TY  - CHAP
TI  - Linked Data for Learning Analytics: Potentials and Challenges
AU  - Zouaq, Amal
AU  - Jovanović, Jelena
AU  - Gašević, Dragan
AU  - Joksimović, Srećko
T2  - The Handbook of Learning Analytics
A2  - Lang, Charles
A2  - Siemens, George
A2  - Wise, Alyssa Friend
A2  - Gaševic, Dragan
AB  - 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  elds.
CY  - Alberta, Canada
DA  - 2017///
PY  - 2017
ET  - 1
SP  - 347
EP  - 355
PB  - Society for Learning Analytics Research (SoLAR)
SN  - 978-0-9952408-0-3
UR  - http://solaresearch.org/hla-17/hla17-chapter1
ER  -