The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Context-Aware Recommender Systems for Learning: A Survey and Future Challenges
Open accessAbstract
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.
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Cites
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Cited by
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning
Tarus, J. K., Niu, Z., & Mustafa, G. (2018) · Artificial Intelligence Review · doi:10.1007/s10462-017-9539-5 · Cited by 490
Recommender systems in the healthcare domain: state-of-the-art and research issues
Tran, T. N. T., Felfernig, A., Trattner, C., & Holzinger, A. (2021) · Journal of Intelligent Information Systems · doi:10.1007/s10844-020-00633-6 · Cited by 303
Progress in context-aware recommender systems — An overview
Raza, S., & Ding, C. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2019.01.001 · Cited by 151
An overview of adaptive e-learning systems
Ennouamani, S., & Mahani, Z. (2017) · 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/intelcis.2017.8260060 · Cited by 105
Xu, L., & Sang, X. (2022) · Scientific Programming · doi:10.1155/2022/4823828 · Cited by 23
Metadata
- Authors
- Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E.
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2012-10
- Volume
- 5
- Issue
- 4
- Pages
- 318-335
- ISSN
- 1939-1382 (ISSN-L)
- 2372-0050
- OpenAlex
- W2128070164
- Cited by
- 560
- References
- 77
- FWCI
- 98.72
- Type
- journal article
- Language
- en
- Open access
- green
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- Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E. (2012). Context-Aware Recommender Systems for Learning: A Survey and Future Challenges. IEEE Transactions on Learning Technologies. https://doi.org/10.1109/tlt.2012.11
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
