Handbook of Learning Analytics
Linked Data for Learning Analytics:
The Case of the LAK Dataset
Davide Taibi & Stefan Dietze
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.