Enhancing Student Performance Prediction Using Real-Time Data and Explainable Artificial Intelligence in Higher Education

Abstract

Accurate prediction of student performance has become a strategic priority in higher education, driven by increasing learner diversity, rising dropout rates, and limited institutional resources. Traditional statistical models based on historical records are limited in capturing the dynamic and multidimensional nature of student learning. This study proposes a real-time predictive framework that integrates machine learning with explainable artificial intelligence to achieve both predictive accuracy and interpretability. Data were collected from 2175 undergraduate students from Faculty of Humanities & Social Sciences, University of Ruhuna across five course modules, combining institutional academic and learning management system records with a structured survey capturing financial and psychosocial indicators. After a systematic preprocessing pipeline, five machine learning classifiers were developed and evaluated, including Logistic Regression, Decision Tree, K Nearest Neighbours, Support Vector Machine, and Random Forest. Model performance was assessed using accuracy, precision, recall, F1 score, and area under the curve. Random Forest achieved the highest accuracy of 96.92%, while Support Vector Machine achieved the strongest discriminative capability with the highest area under the curve. To address interpretability, SHapley Additive exPlanations provided global feature attributions, and Local Interpretable Model Agnostic Explanations generated case-specific insights. Results consistently identified attendance and assignment performance as dominant predictors, while behavioural engagement and financial and psychological dimensions offered complementary contributions. A decision support dashboard was designed to operationalize these insights by enabling early identification of at-risk learners, explaining contributing factors, and generating individualized reports for timely intervention. The proposed framework demonstrates that robust predictive accuracy and interpretability can be achieved simultaneously, providing a practical tool for enhancing student retention, equitable support, and efficient resource allocation in higher education.