Abstract
Performance of Web-based learning environment depends on the degree it is adjusted into needs of virtual learning community members. Creating groups of students with similar needs enables to differentiate appropriately the environment features. Each new student, who joins the community, should obtain the recommendation of the group of colleagues with similar characteristics. In the paper, it is considered using fuzzy logic for modeling student clusters. As the representation of each group, we assume fuzzy numbers connected with learner attributes ranked according to their cardinality. Recommendations for new students are determined taking into account similarity of their dominant features and the highest ranked attributes of groups. The presented approach is investigated, taking into considerations learning style dimensions as student attributes. The method is evaluated on the basis of experimental results obtained for data of different groups of real students.
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Myszkorowski, K., Zakrzewska, D. (2013). Using Fuzzy Logic for Recommending Groups in E-Learning Systems. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_67
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