Machine Learning Models for Early Identification of At-Risk Students

Abstract

This study developed and evaluated supervised machine learning models for the early identification of academically at-risk students using selected academic performance indicators. Anchored on Educational Data Mining and supervised machine learning classification, the study employed a quantitative experimental research design using a synthetic dataset composed of 100 student records. The dataset included attendance rate, quiz average score, assignment completion rate, midterm examination score, final examination score, and class participation score. The students were classified as either at-risk or not at-risk based on patterns of attendance and academic performance. Data preprocessing involved mean imputation, Min-Max Scaling, categorical encoding, and an 80% training and 20% testing split. Logistic Regression, Decision Tree, and Random Forest models were implemented and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. Results showed that Random Forest achieved the highest predictive performance, with 93% accuracy, 0.92 precision, 0.91 recall, 0.91 F1-score, and 0.95 ROC-AUC. The model correctly classified 46 non-at-risk students and 47 at-risk students, with only 4 false positives and 3 false negatives. The study concludes that machine learning can support early warning systems and improve educational decision-making by helping institutions identify students who may need timely academic support. Future studies should use larger real-world institutional datasets and include broader learner-related variables to improve model generalization. The study supports SDG 4, Quality Education, and SDG 9, Industry, Innovation and Infrastructure, by promoting data-driven academic intervention and educational innovation. Its sustainability impact is linked to educational, institutional, and technological sustainability through improved student monitoring, support planning, and evidence-based decision-making.