Predict Rich Learner School Dropout and Improve Success Rates Using a Datamining Study and Machine Learning Algorithms

This study focuses on developing a Data Mining (DM) system enhanced with machine learning to support school management. Its primary objective is to assist educators and policymakers in making well-informed decisions to tackle school dropout issues and boost success rates at key educational milestones in Morocco, particularly in the 3rd year of college. The system aims to identify students needing additional academic support five months before exams to prevent course repetition or discontinuation. The initiative seeks to enable schools and local authorities to work more effectively throughout the school year by pinpointing areas that need reinforcement to enhance year-end success rates and student achievements. The development of this project involves gathering and analyzing data from existing departmental information systems, utilizing classification and regression algorithms to foresee student performance and identify those at risk of dropping out. The system also considers student performance at the end of each certificate level.