Title: Context-aware recommender systems for learning: a survey and future challenges
Authors: Verbert, Katrien ×
Manouselis, Nikos
Ochoa, Xavier
Wolpers, Martin
Drachsler, Hendrik
Bosnic, Ivana
Duval, Erik #
Issue Date: 2012
Publisher: IEEE
Series Title: IEEE Transactions on Learning Technologies vol:5 issue:4 pages:318-335
Abstract: Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community in 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.
ISSN: 1939-1382
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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