Evaluating Machine Learning Models for Predicting Graduation Timelines in Moroccan Universities

The escalating student numbers in Moroccan universities have intensified the complexities of managing on-time graduation. In this context, Machine learning methodologies were utilized to analyze the patterns and predict on-time graduation rates in a comprehensive manner. Our dataset comprised information from 5236 bachelor students who graduated in the years 2020 and 2021 from the Faculty of Law, Economic, and Social Sciences at Moulay Ismail University. The dataset incorporated a diverse range of student attributes including age, marital status, gender, nationality, socio-economic category of parents, profession, disability status, province of residence, high school diploma attainment, and academic honors, all contributing to a comprehensive understanding of the factors influencing graduation outcomes. Implementation and evaluation of the performance of five different machine learning models: Support Vector Machines, Decision Tree, Naive Bayes, Logistic Regression, and Random Forest, were carried out. These models were assessed based on their classification reports, confusion matrices, and Receiver Operating Characteristic (ROC) curves. From the findings, the Random Forest model emerged as the most accurate in predicting on-time graduation, showcasing the highest accuracy and ROC AUC score. Despite these promising results, it is believed that performance enhancements can be achieved through further tuning and preprocessing of the dataset. Insights from this study could enable Moroccan universities, among others, to better comprehend the factors influencing on-time graduation and implement appropriate measures to improve academic outcomes.