Title: Toward a hybrid machine learning approach for extracting and clustering learners' behaviours in adaptive educational system

Authors: Ouafae El Aissaoui; Yasser El Alami El Madani; Lahcen Oughdir; Ahmed Dakkak; Youssouf El Allioui

Addresses: LSI, FPT, Sidi Mohamed Ben Abdellah University, Fez, Morocco ' ENSIAS, Mohammed V University, Rabat, Morocco ' LSI, FPT, Sidi Mohamed Ben Abdellah University, Fez, Morocco ' LSI, FPT, Sidi Mohamed Ben Abdellah University, Fez, Morocco ' LS3M, FPK, Hassan First University, Khouribga, Morocco

Abstract: The learning style is a vital learner's characteristic that must be considered while recommending learning materials. In this paper we have proposed an approach to identify learning styles automatically. The first step of the proposed approach aims to preprocess the data extracted from the log file of the E-learning environment and capture the learners' sequences. The captured learners' sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system's log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results.

Keywords: unsupervised algorithm; supervised algorithm; K-means; naïve Bayes; adaptive E-learning systems; Felder-Silverman learning style model.

DOI: 10.1504/IJCSM.2020.111113

International Journal of Computing Science and Mathematics, 2020 Vol.12 No.2, pp.117 - 131

Received: 19 Nov 2018
Accepted: 16 Jan 2019
Published online: 10 Nov 2020 *

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