Predicting baccalaureate student result to prevent failure: a hybrid model approach

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.