Context-aware recommender systems for learning: A survey and future challenges

Katrien Verbert, Nikos Manouselis, Xavier Ochoa, Martin Wolpers, Hendrik Drachsler, Ivana Bosnic, Erik Duval

Research output: Contribution to journalArticlepeer-review

Abstract

Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.

Original languageEnglish (US)
Article number6189308
Pages (from-to)318-335
Number of pages18
JournalIEEE Transactions on Learning Technologies
Volume5
Issue number4
DOIs
StatePublished - 2012

Keywords

  • Adaptive and intelligent educational systems
  • personalized e-learning
  • system applications and experience

ASJC Scopus subject areas

  • Education
  • Engineering(all)
  • Computer Science Applications

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