Manouselis / Drachsler / Verbert Recommender Systems for Learning

1. Auflage 2012
ISBN: 978-1-4614-4361-2
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 76 Seiten, eBook

Reihe: SpringerBriefs in Electrical and Computer Engineering

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.

Zielgruppe

Research

Autoren/Hrsg.

Manouselis, Nikos

Drachsler, Hendrik

Verbert, Katrien

Duval, Erik

Inhaltsverzeichnis

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