MACHINE LEARNING ALGORITHMS FOR PREDICTING STUDENTS' ACADEMIC PERFORMANCE IN EDUCATIONAL - Salmiyah,Fatimah (An-Najah National University, 2025)
Student performance prediction has been one of the important works in educational data mining, due to the possibility of early detection, intervention, and informed decision-making in academics. The purpose of this study is to improve the accuracy of predicting student performance by using seven machine learning models—Decision Tree, Random Forest, Linear Regression, Neural Network, Support Vector Machines (SVM), Logistic Regression, Naive Bayes and five feature selection techniques : Particle Swarm Optimization (PSO), Lasso, Wrapper Method, SelectKBest and SelectPercentile. The research investigates how student outcomes are associated with such factors as previous educational experience, parental education, past educational failures, attendance, residence and participation in extra-curriculum activities. The results show that Linear Regression combined with SelectKBest achieved the highest accuracy of 93.5% . The performance of the model was further optimized by hyperparameter tuning (GridSearchCV) and k-fold cross-validation, which increased both prediction accuracy and model robustness. The results highlight the role of feature selection in maximizing model performance and offer practical guidance for higher education institutions interested in implementing predictive analytics to enhance student success.
خوارزميات التعلم الآلي لتوقع الأداء الأكاديمي للطلاب في التنقيب عن البيانات التعليمية