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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Essayad, Abdesslam
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Computer Science & IT Engineering

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Predicting baccalaureate student result to prevent failure: a hybrid model approach Essayad, Abdesslam; Moulay Abdella, Kassimi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp764-774

Abstract

The Moroccan Ministry of National Education has seen substantial modifications over the previous ten years, which have contributed to improving the quality of education. However, there is a discrepancy in the percentage of academic achievement between the regional directorates and educational institutions. Machine learning techniques have become a powerful tool for proactively predicting student admission. The goal of our paper is to build machine learning models using various algorithms to predict the final baccalaureate school year outcomes. We compare regression and classification to find the reasons behind students' failure and to choose an appropriate model for predicting the results. This helps decision-makers make appropriate interventions.
Co-Authors Moulay Abdella, Kassimi