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

Author

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

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.

Suggested Citation

  • Marivel B. Go & Rodolfo A. Golbin Junior & Severina P. Velos & Johnry P. Dayupay & Feliciana G. Cababat & Jeem Clyde C. Baird & Hazna Quiñanola, 2023. "A data mining approach to classifying e-learning satisfaction of higher education students: a Philippine case," International Journal of Innovation and Learning, Inderscience Enterprises Ltd, vol. 33(3), pages 314-329.
    • Handle: RePEc:ids:ijilea:v:33:y:2023:i:3:p:314-329
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    Cited by:

    1. Romeo, Jr. E. Bejar, 2024. "Precision in Progress: Leveraging Data Mining Technique to Empower Career Path Selection for Incoming Senior High School Students," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(1), pages 178-191, January.

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