Recommender Systems for Learning

1. Front Matter

   Pages i-xi

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2. Introduction and Background

   • Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval

   Pages 1-20

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3. TEL as a Recommendation Context

   • Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval

   Pages 21-36

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4. Survey and Analysis of TEL Recommender Systems

   • Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval

   Pages 37-61

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5. Challenges and Outlook

   • Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval

   Pages 63-76

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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.

• Recommender systems
• TEL datasets
• personalization
• recommendation algorithms
• technology enhanced learning (TEL)

• Agro-Know Technologies, Athens, Greece

  Nikos Manouselis

• the Netherlands, Centre for Learning Sciences, Open University of, Heerlen, Netherlands

  Hendrik Drachsler

• , Department of Computer Science, KU Leuven, Leuven, Belgium

  Katrien Verbert, Erik Duval

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