Recommender Systems in Technology Enhanced Learning

Nikos Manouselis*, Hendrik Drachsler, Riina Vuorikari, Hans Hummel, Rob Koper

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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    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 languageEnglish
    Title of host publicationRecommender Systems Handbook
    EditorsFrancesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor
    Place of PublicationBoston
    PublisherSpringer Science + Business Media
    Pages387-415
    Number of pages29
    Edition1
    ISBN (Electronic)9780387858203
    ISBN (Print)9780387858197
    DOIs
    Publication statusPublished - 2011

    Keywords

    • state-of-the-art
    • recommender systems
    • adaptive hypermedia
    • Learning Networks
    • personalization

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