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Context-Aware Recommender Systems for Learning: A Survey and Future Challenges (2012)
| Venue: | IEEE Trans. Learn |
| Citations: | 16 - 0 self |
BibTeX
@ARTICLE{Verbert12context-awarerecommender,
author = {Katrien Verbert and Nikos Manouselis and Xavier Ochoa and Martin Wolpers and Hendrik Drachsler and Ivana Bosnic and Student Member and Erik Duval},
title = {Context-Aware Recommender Systems for Learning: A Survey and Future Challenges},
journal = {IEEE Trans. Learn},
year = {2012}
}
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Abstract
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. Index Terms—Adaptive and Intelligent Educational Systems, Personalized E-Learning, System Applications and Experience. F
Keyphrases
context-aware recommender system future challenge tel recommender system intelligent system suitable resource last decade intelligent educational system abstract recommender system personalized e-learning technology enhanced learning index term adaptive relevant context dimension contextual information overwhelming variety outline area tel application system application context framework recommendation process current work major interest survey result rich environment promising approach teaching task
