Handbook of Learning Analytics

Chapter 29

Handbook of Learning Analytics
First Edition

Linked Data for Learning Analytics:
The Case of the LAK Dataset

Davide Taibi & Stefan Dietze


Abstract

The opportunities of learning analytics (LA) are strongly constrained by the availability and quality of appropriate data. While the interpretation of data is one of the key requirements for analyzing it, sharing and reusing data are also crucial factors for validating LA techniques and methods at scale and in a variety of contexts. Linked data (LD) principles and techniques, based on established W3C standards (e.g., RDF, SPARQL), offer an approach for facilitating both interpretability and reusability of data on the Web and as such, are a fundamental ingredient in the widespread adoption of LA in industry and academia. In this chapter, we provide an overview of the opportunities of LD in LA and educational data mining (EDM) and introduce the specific example of LD applied to the Learning Analytics and Knowledge (LAK) Dataset. The LAK dataset provides access to a near-complete corpus of scholarly works in the LA field, exposed through rigorously applying LD principles. As such, it provides a focal point for investigating central results, methods, tools, and theories of the LA community and their evolution over time.

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About this Chapter

Title
Linked Data for Learning Analytics: The Case of the LAK Dataset

Book Title
Handbook of Learning Analytics

Pages
pp. 337-345

Copyright
2017

DOI
10.18608/hla17.029

ISBN
978-0-9952408-0-3

Publisher
Society for Learning Analytics Research

Authors
Davide Taibi1
Stefan Dietze2

Author Affiliations
1. Istituto per le Tecnologie Didattiche, Consiglio Nazionale delle Ricerche, Italy
2. L3S Research Center, Germany

Editors
Charles Lang3
George Siemens4
Alyssa Wise5
Dragan Gašević6

Editor Affiliations
3. Teachers College, Columbia University, USA
4. LINK Research Lab, University of Texas at Arlington, USA
5. Learning Analytics Research Network, New York University, USA
6. Schools of Education and Informatics, University of Edinburgh, UK


Society for Learning Analytics Research (SoLAR)