Abstract
The rapid advances in Web technologies are promoting the development of new pedagogic models based on virtual teaching. To achieve this personalized services are necessary to provide the users with relevant information, according to their preferences and needs. Recommender systems can be used in an academic environment to improve and assist users in their teaching-learning processes. In this paper we propose a fuzzy linguistic recommender system to facilitate learners the access to e-learning resources interesting for them. By suggesting didactic resources according to the learner’s specific needs, a relevance-guided learning is encouraged, influencing directly the teaching-learning process. We propose the combination of the relevance degree of a resource for a user with its quality in order to generate more profitable and accurate recommendations. In addition to that, we present a computer-supported learning system to teach students the principles and concepts of recommender systems.
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Acknowledgments
This paper has been developed with the financing of Projects UJA2013/08/41, TIN2013-40658-P, TIC5299, TIC-5991, TIN2012-36951 co-financed by FEDER and TIC6109.
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Porcel, C., Lizarte, M.J., Bernabé-Moreno, J., Herrera-Viedma, E. (2015). A Learning Web Platform Based on a Fuzzy Linguistic Recommender System to Help Students to Learn Recommendation Techniques. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_57
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DOI: https://doi.org/10.1007/978-3-319-24834-9_57
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