Context-aware Recommender Systems for Learning: a Survey and Future Challenges
Printer-friendly versionPDF versionTitle: 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
Abstract: Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community in thelast decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promisingapproach 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. Suchcontextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs ofusers, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work inTEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a contextframework 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).
Authors: Verbert, Katrien; Manouselis, Nikos; Xavier, Ochoa; Wolpers, Martin; Drachsler, Hendrik; Ivana, Bosnic; Erik, Duval
Abstract: Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community in thelast decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promisingapproach 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. Suchcontextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs ofusers, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work inTEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a contextframework 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).
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