Panorama of Recommender Systems to Support Learning

  • Hendrik Drachsler
  • Katrien Verbert
  • Olga C. Santos
  • Nikos Manouselis

Abstract

This chapter presents an analysis of recommender systems in Technology-Enhanced Learning along their 15 years existence (2000–2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into seven clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.

Keywords

Recommender systems Technology enhanced learning Classification framework State-of-the-art review Educational datasets Learning analytics Educational data mining Personalisation Trend analysis Future challenges 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Hendrik Drachsler
    • 1
  • Katrien Verbert
    • 2
    • 3
  • Olga C. Santos
    • 4
  • Nikos Manouselis
    • 5
  1. 1.Welten Institute Research Centre for Learning, Teaching and TechnologyOpen University of the NetherlandsHeerlenThe Netherlands
  2. 2.Department of Computer ScienceKU LeuvenLeuvenBelgium
  3. 3.Department of Computer ScienceVrije Universiteit BrusselBrusselBelgium
  4. 4.aDeNu Research Group, UNEDMadridSpain
  5. 5.Agro-KnowVrilissiaGreece

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