An approach-based ensemble methods to predict school performance for Moroccan students

Abstract

Education is a key factor in Morocco's development, with school performance serving as a critical measure of the education system’s quality. However, disparities in student outcomes remain, influenced by socioeconomic, demographic, and infrastructural factors. Our study aims to develop a predictive model to assess and improve school performance in Morocco using ensemble machine learning techniques, focusing on the stacking approach. Data from the Massar platform includes variables such as gender, age, type of school, parental occupation, academic results, and residential area. After rigorous data cleaning and preprocessing, a stacking model was created by combining predictions from five base models: random forest, gradient boosting, k-nearest neighbors (KNN), support vector machine (SVM), and multi-layer perceptron (MLP). A random forest metamodel was used to integrate these results. The experimental results of the paper demonstrate the effectiveness of our approach. The stacking model achieved an accuracy of 78.70%, surpassing the individual base models. The meta-model demonstrated strong reliability, achieving an F1 score of 78.62% while reducing false negatives and ensuring balanced predictions. Among the base models, neural networks showed the best performance, achieving the highest predictive accuracy. This research highlights the potential of stacking methods for predicting school performance. Incorporating additional variables, such as parental education and teacher attributes, could further refine the model and enhance Morocco’s educational outcomes.