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Recommender Systems in Technology Enhanced Learning

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Abstract

Technology enhanced learning (TEL) aims to design, develop and test socio-technical 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 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.

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Acknowledgements

Research of N. Manouselis was funded with support by the European Commission and more specifically, the project ECP-2006-EDU-410012 ‘Organic.Edunet: A Multilingual Federation of Learning Repositories with Quality Content for the Awareness and Education of European Youth about Organic Agriculture and Agroecology’ of the eContentplus Programme. Research of H. Drachsler was funded with support by the European Commission and more specifically, the project IST 027087 ‘TENCompetence’ of the FP6 Programme. Riina Vuorikari thanks the HS-säätiö for the stipend.

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Authors and Affiliations

  1. Greek Research and Technology Network (GRNET S.A.), 56 Messogeion Av., 115 27, Athens, Greece

    Nikos Manouselis

  2. Centre for Learning Sciences and Technologies (CELSTEC), Open Universiteit Nederland, Heerlen, Netherlands

    Hendrik Drachsler, Hans Hummel & Rob Koper

  3. European Schoolnet (EUN), 24, Rue Paul Emile Janson, 1050, Brussels, Belgium

    Riina Vuorikari

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  1. Nikos Manouselis
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Correspondence to Nikos Manouselis .

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Editors and Affiliations

  1. , Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani 3, Bolzano, 39100, Italy

    Francesco Ricci

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

  3. Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

  4. School of Communication,, Information & Library Studies, Rutgers University, Huntington Street 4, New Brunswick, 08901-1071, New Jersey, USA

    Paul B. Kantor

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Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R. (2011). Recommender Systems in Technology Enhanced Learning. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_12

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