The Use of EDM on Predicting the Rate of Moroccan University Dropouts: Sharia university, Fez as A Case Study

  • Authors

    • Moulay Hachem Alaoui Harouni
    • El-Kaber Hachem
    • Cherif Ziti
    • Mustapha Bassiri
    2018-12-06
    https://doi.org/10.14419/ijet.v7i4.32.23245
  • EDM, dropouts, Bayes theorem, classification, self-evaluation.

  • Abstract

    The present article is going to highlight the concept of educational data mining (EDM) and discuss how it can be mainly used to predict Shariaa University dropouts; in other words, it may demonstrate how to enable academics to help students not to drop out their studies, the collected data were taken from our previous study of dropouts at Shariaa University, Fez-Morocco. Using the Bayes theorem that is stimulated from the statistical learning theory to resolve classification problems, we can determine the strengths and weaknesses of those students who are probably to dropout.

     The reasons of conducting this second research on dropping out are to provide the support of continuous university self-evaluation and improvement through using educational data mining, and to help those students who will be expected to leave their university.

     

     

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  • How to Cite

    Hachem Alaoui Harouni, M., Hachem, E.-K., Ziti, C., & Bassiri, M. (2018). The Use of EDM on Predicting the Rate of Moroccan University Dropouts: Sharia university, Fez as A Case Study. International Journal of Engineering & Technology, 7(4.32), 53-56. https://doi.org/10.14419/ijet.v7i4.32.23245

    Received date: 2018-12-06

    Accepted date: 2018-12-06

    Published date: 2018-12-06