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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/3086

Title: Recommender Systems in Technology Enhanced Learning
Authors: Manouselis, Nikos
Drachsler, Hendrik
Vuorikari, Riina
Hummel, Hans
Koper, Rob
Keywords: state-of-the-art
recommender systems
recommender systems
adaptive hypermedia
Learning Networks
personalization
Issue Date: 21-Dec-2010
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.
Description: Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. G. K., & Koper, R. (2011). Recommender Systems in Technology Enhanced Learning. In P. B. Kantor, F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 387-415). Berlin: Springer.
URI: http://hdl.handle.net/1820/3086
Appears in Collections:1. LN: Publications and Preprints

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