A data mining approach to classifying e-learning satisfaction of higher education students: a Philippine case

Marivel B. Go, Rodolfo A. Golbin Junior, Severina P. Velos, Johnry P. Dayupay, Feliciana G. Cababat, Jeem Clyde C. Baird and Hazna Quiñanola

International Journal of Innovation and Learning, 2023, vol. 33, issue 3, 314-329

Abstract: E-learning has become increasingly important for higher education institutions. It offers an alternative mode of learning for educational institutions during critical situations such as the COVID-19 pandemic. While e-learning has gained growing attention in the current literature, a significant gap is left unaddressed for emerging economies, particularly the Philippines. In this paper, the factors of e-learning in a higher education institution in the Philippines are analysed. A data mining approach is used to predict the satisfaction of higher education students given eleven features of the subjects. Four classifiers: 1) logistic regression; 2) support vector machine; 3) multilayer perceptron; 4) decision tree, are used to develop the predictive models. The findings reveal that the features considered in this paper can be used to accurately predict the student satisfaction towards e-learning of higher education students in the Philippines.

Keywords: e-learning; machine learning; data mining for e-learning; e-learning in the Philippines. (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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