Abstract
Technology enhanced learning (TEL) aims to design, develop and test sociotechnical
innovations that will support and enhance learning practices of both individuals
and organizations. It is therefore an application domain that generally
covers technologies that support all forms of teaching and learning activities.
Since information retrieval (in terms of searching for relevant learning resources
to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This chapter attempts to provide
an introduction to recommender systems for TEL settings, as well as to highlight
their particularities compared to recommender systems for other application
domains.
Original language | English |
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Title of host publication | Recommender Systems Handbook |
Editors | Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor |
Place of Publication | Boston |
Publisher | Springer Science + Business Media |
Pages | 387-415 |
Number of pages | 29 |
Edition | 1 |
ISBN (Electronic) | 9780387858203 |
ISBN (Print) | 9780387858197 |
DOIs | |
Publication status | Published - 2011 |
Keywords
- state-of-the-art
- recommender systems
- adaptive hypermedia
- Learning Networks
- personalization