Development of an Early Warning System for Predicting Student Academic Failure Using PSO-Based Machine Learning

Abstract

Student academic failure is a critical issue in higher education, as it affects graduation rates and the overall quality of an institution. Early identification of students at risk is essential to enable timely academic interventions. This study aims to develop a predictive model to identify students at risk of academic failure using machine learning techniques. The dataset used in this research was obtained from the UCI Machine Learning Repository and includes students’ demographic, socio-economic, and academic attributes. This study applies Particle Swarm Optimization integrated with Mutual Information (PSO-MI) as a feature selection method. It compares the performance of K-Nearest Neighbor (KNN) and Neural Network (NN) classification algorithms. The feature selection process identified 12 relevant features related to students' academic performance and administrative information. Model evaluation was conducted using two validation schemes: split validation with an 80:20 ratio and k-fold cross-validation, and performance was assessed using precision, recall, and F1 Score metrics. The experimental results show that the Neural Network model with PSO-MI-based feature selection consistently outperformed the KNN model under both validation schemes. In the cross-validation experiment, the Neural Network model achieved an accuracy of 0.91, a precision of 0.91, a recall of 0.89, and an F1-score of 0.90, indicating better performance in identifying students at risk of dropout. These findings demonstrate that integrating PSO-based feature selection with Neural Network classification offers a promising approach to predicting academic failure. The proposed framework can support the development of early warning systems to help educational institutions identify at-risk students and implement timely academic interventions