Abstract
Personalized e-learning system should be tailored into student needs, which usually differ even among learners, who attend the same course and have similar technical skills. In the chapter, it is proposed the system architecture, in which teaching paths as well as proper layouts are adjusted to groups of students with similar preferences, created by application of clustering techniques. Learner models are based on dominant learning style dimensions, according to which students focus on different types of information and show different performances in educational process. Extension of the model by including usability preferences is investigated. There are examined different clustering techniques to obtain groups of the best quality. It is presented the algorithm that will fulfill tutor requirements especially concerning the choice of parameters. Some experimental results for real groups of students and different algorithms are described and discussed.
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Zakrzewska, D. (2009). Cluster Analysis in Personalized E-Learning Systems. In: Nguyen, N.T., Szczerbicki, E. (eds) Intelligent Systems for Knowledge Management. Studies in Computational Intelligence, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04170-9_10
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DOI: https://doi.org/10.1007/978-3-642-04170-9_10
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