<![CDATA[School dropout is a major dare for Moroccan education systems. It generates profound repercussions, both socially and economically, affecting both individuals and the entire community. To assess the risks associated with this phenomenon, it is essential to use innovative technologies, relying in particular on statistical methods and artificial intelligence models capable of providing effective results using available data. These methods offer the possibility of identifying the factors influencing the educational phenomenon and anticipating the associated risks. To understand the effect of scholars’ social, economic, and personal influences on school dropout, we propose in this work a new classifier, based on an improvement of support vector machines (SVM) and on the ABC algorithm. We also present a regression model designed to estimate academic performance based on the average grade obtained. We also propose a new regression model to predict the degree of academic achievement. The proposed model was validated on a real dataset, compiled from a questionnaire distributed to Moroccan secondary school students. Furthermore, several comparative studies were conducted with recent approaches to assess the performance of the proposed methods. All the experimental results confirm the robustness and improved performance of the developed methods, particularly with respect to accuracy and predictive capability.]]>
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