TY - JOUR
T1 - Context-aware recommender systems for learning
T2 - A survey and future challenges
AU - Verbert, Katrien
AU - Manouselis, Nikos
AU - Ochoa, Xavier
AU - Wolpers, Martin
AU - Drachsler, Hendrik
AU - Bosnic, Ivana
AU - Duval, Erik
N1 - Funding Information:
The authors would like to thank Shlomo Berkovsky for his valuable comments on earlier versions of this manuscript. In addition, we would like to thank the anonymous reviewers for their suggestions that helped to improve this work to a great extent. Part of this work has been supported by the EU FP7 STELLAR Network of Excellence (grant agreement no. 231913). Katrien Verbert is a Postdoctoral Fellow of the Research Foundation—Flanders (FWO). The work of Nikos Manouselis has been funded with support by the EU project VOA3R - 250525 of the CIP PSP Programme (http://voa3r.eu). The work of Martin Wolpers has received funding from the EC Seventh Framework Programme (FP7/2007-2013) under grant agreement no 231396 (ROLE). The contribution of Xavier Ochoa was supported by VLIR through the RIP Project ZEIN2010RIP09. The work of Hendrik Drachsler was supported by the Netherlands Laboratory for Lifelong Learning (NELLL) within the AlterEgo project.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Adaptive and intelligent educational systems
KW - personalized e-learning
KW - system applications and experience
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U2 - 10.1109/TLT.2012.11
DO - 10.1109/TLT.2012.11
M3 - Article
AN - SCOPUS:84870994062
SN - 1939-1382
VL - 5
SP - 318
EP - 335
JO - IEEE Transactions on Learning Technologies
JF - IEEE Transactions on Learning Technologies
IS - 4
M1 - 6189308
ER -