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Title: Context-aware Recommender Systems for Learning: a Survey and Future Challenges
Authors: Verbert, Katrien
Manouselis, Nikos
Xavier, Ochoa
Wolpers, Martin
Drachsler, Hendrik
Ivana, Bosnic
Erik, Duval
Keywords: Adaptive and Intelligent Educational Systems
Personalized E-Learning
System Applications and Experience
recommender systems
Issue Date: 8-Dec-2011
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.
Description: Verbert, K., Manouselis, N., Xavier, O., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E. (accepted). Context-aware Recommender Systems for Learning: a Survey and Future Challenges. IEEE Transactions on Learning Technologies (TLT).
Appears in Collections:1. LN: Publications and Preprints

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