Skip to main content
SpringerLink
Log in

Recommender Systems for Learning

  • Book
  • © 2013

Overview

Authors:
  1. Nikos Manouselis

    1. Agro-Know Technologies, Athens, Greece

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Hendrik Drachsler

    1. the Netherlands, Centre for Learning Sciences, Open University of, Heerlen, Netherlands

    View author publications

    You can also search for this author in PubMed   Google Scholar

  3. Katrien Verbert

    1. , Department of Computer Science, KU Leuven, Leuven, Belgium

    View author publications

    You can also search for this author in PubMed   Google Scholar

  4. Erik Duval

    1. , Department of Computer Science, KU Leuven, Leuven, Belgium

    View author publications

    You can also search for this author in PubMed   Google Scholar

Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (4 chapters)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Introduction and Background

    • Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval
    Pages 1-20

  3. TEL as a Recommendation Context

    • Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval
    Pages 21-36

  4. Survey and Analysis of TEL Recommender Systems

    • Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval
    Pages 37-61

  5. Challenges and Outlook

    • Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval
    Pages 63-76
Back to top

Keywords

About this book

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.

Authors and Affiliations

  • 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

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation