Abstract
Personalisation, adaptation and recommendation are central features
of TEL environments. In this context, information retrieval techniques are applied
as part of TEL recommender systems to filter and recommend learning resources
or peer learners according to user preferences and requirements. However,
the suitability and scope of possible recommendations is fundamentally
dependent on the quality and quantity of available data, for instance, metadata
about TEL resources as well as users. On the other hand, throughout the last
years, the Linked Data (LD) movement has succeeded to provide a vast body of
well-interlinked and publicly accessible Web data. This in particular includes
Linked Data of explicit or implicit educational nature. The potential of LD to
facilitate TEL recommender systems research and practice is discussed in this
paper. In particular, an overview of most relevant LD sources and techniques is
provided, together with a discussion of their potential for the TEL domain in
general and TEL recommender systems in particular. Results from highly related
European projects are presented and discussed together with an analysis of
prevailing challenges and preliminary solutions.
| Original language | English |
|---|---|
| Title of host publication | Recommender Systems for Technology Enhanced Learning |
| Subtitle of host publication | Research Trends & Applications |
| Editors | N. Manouselis, H. Drachsler, K. Verbert, O.C. Santos |
| Publisher | Springer US |
| Pages | 47-75 |
| ISBN (Electronic) | 978-1-4939-0530-0 |
| ISBN (Print) | 978-1-4939-0529-4, 978-1-4939-4656-3 |
| Publication status | Published - 17 Dec 2014 |
Keywords
- Linked Data
- Education
- Semantic Web
- Technology-Enhanced Learning
- Data Consolidation
- Data Integration