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Effects of Performance Clustering in User Modelling for Learning Style Knowledge Representation

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Advances and Trends in Artificial Intelligence. From Theory to Practice (IEA/AIE 2021)


The transformation of education from the era of face-to-face teaching to the era of e-learning has promoted the rise of technological approaches for educational teaching. This new educational norm is currently confronting challenges especially in terms of analysing student performance in e-learning platforms. Furthermore, differences in how students receive and process learning information has focused attention on analysing student learning style. Therefore, this research has introduced two important investigations, which are analysing the relationship between student learning style behaviours and their learning performance in e-learning platforms, as well as combining the K-means algorithm with the Principal Component Analysis (PCA) feature reduction technique to produce a clustering model. By comparing based on Felder-Silverman (FS) learning style dimensions, students who have similar learning style dimensions would produce similar learning performance in the e-learning platform. The PCA method has successfully increased the silhouette coefficient of the K-means clustering model. The clustering model grouped students into different clusters based on student learning characteristics.

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The authors appreciate the financial support given by the Fundamental Research Grant Scheme, FRGS/1/2019/SS06/MMU/02/4.

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Correspondence to Sin-Ban Ho .

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Teoh, CW. et al. (2021). Effects of Performance Clustering in User Modelling for Learning Style Knowledge Representation. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham.

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  • Print ISBN: 978-3-030-79462-0

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