Predictive Modeling of Student Performance Using RFECV-RF for Feature Selection and Machine Learning Techniques

Abstract

Predicting student performance has become a strategic challenge for universities, essential for increasing student success rates, retention, and tackling dropout rates. However, the large volume of educational data complicates this task. Therefore, many research projects have focused on using Machine Learning techniques to predict student success. This study aims to propose a performance prediction model for students at IBN ZOHR University in Morocco. We employ a combination of Random Forest and Recursive Feature Elimination with Cross-Validation (RFECV-RF) for optimal feature selection. Using these features, we build classification models with several Machine Learning algorithms, including AdaBoost, Logistic Regression (LR), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Support Vector Machines (SVM), and Decision Trees (DT). Our results show that the SVM model, using the 8 features selected by RFECV-RF, outperforms the other classifiers with an accuracy of 87%. This demonstrates the effectiveness and efficiency of our feature selection method and the superiority of the SVM model in predicting student performance.