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


Teoh, Chin Wei and Ho, Sin Ban and Dollmat, Khairi Shazwan and Chai, Ian and Mohd Isa, Wan Noorshahida and Tan, Chuie Hong and Teh, Sek Kit and Raihan, Manzoor Shahida (2021) Effects of Performance Clustering in User Modelling for Learning Style Knowledge Representation. Lecture Notes in Computer Science, 12799. pp. 126-137. ISSN 0302-9743

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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.

Item Type: Article
Uncontrolled Keywords: Principal components analysis
Subjects: Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions: Faculty of Computing and Informatics (FCI)
Faculty of Management (FOM)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 27 Aug 2021 15:54
Last Modified: 27 Aug 2021 15:54
URII: http://shdl.mmu.edu.my/id/eprint/9473


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