Facilitating Scientometrics in Learning Analytics and Educational Data Mining – the LAK Dataset

Article type: Research Article

Authors: Dietze, Stefan^{a; *} | Taibi, Davide^b | d’Aquin, Mathieu^c

Affiliations: [a] L3S Research Center, Appelstrasse 9a, 30167 Hannover, Germany | [b] National Research Council of Italy, Institute for Educational Technologies, via Ugo La Malfa 153, 90146 Palermo, Italy | [c] Knowledge Media Institute, The Open University, Walton Hall, Milton Keynes, UK, MK7 6AA

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: The Learning Analytics and Knowledge (LAK) Dataset represents an unprecedented corpus which exposes a near complete collection of bibliographic resources for a specific research discipline, namely the connected areas of Learning Analytics and Educational Data Mining. Covering over five years of scientific literature from the most relevant conferences and journals, the dataset provides Linked Data about bibliographic metadata as well as full text of the paper body. The latter was enabled through special licensing agreements with ACM for publications not yet available through open access. The dataset has been designed following established Linked Data pattern, reusing established vocabularies and providing links to established schemas and entity coreferences in related datasets. Given the temporal and topic coverage of the dataset, being a near-complete corpus of research publications of a particular discipline, it facilitates scientometric investigations, for instance, about the evolution of a scientific field over time, or correlations with other disciplines, what is documented through its usage in a wide range of scientific studies and applications.

Keywords: Learning Analytics, Educational Data Mining, Linked Data

DOI: 10.3233/SW-150201

Journal: Semantic Web, vol. 8, no. 3, pp. 395-403, 2017