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Recommender Systems for Learning

by Manouselis, Nikos.
Authors: Drachsler, Hendrik.%author. | Verbert, Katrien.%author. | Duval, Erik.%author. | SpringerLink (Online service) Series: SpringerBriefs in Electrical and Computer Engineering, 2191-8112 Physical details: XI, 76 p. 4 illus. online resource. ISBN: 146144361X Subject(s): Computer science. | Information systems. | Computer Science. | Information Systems and Communication Service. | Education (general).
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E-Book E-Book AUM Main Library 005.7 (Browse Shelf) Not for loan

Introduction and Background -- TEL as a recommendation context -- Survey and Analysis of TEL Recommender Systems -- Challenges and Outlook.

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. 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 brief 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.

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