Predicting Student Success with Heterogeneous Graph Deep Learning and Machine Learning Models

Early identification of student success is crucial for enabling timely interventions, reducing dropout rates, and promoting on-time graduation. In educational settings, AI-powered systems have become essential for predicting student performance due to their advanced analytical capabilities. However, effectively leveraging diverse student data to uncover latent and complex patterns remains a key challenge. While prior studies have explored this area, the potential of dynamic data features and multi-category entities has been largely overlooked. To address this gap, we propose a framework that integrates heterogeneous graph deep learning models to enhance early and continuous student performance prediction, using traditional machine learning algorithms for comparison. Our approach employs a graph metapath structure and incorporates dynamic assessment features, which progressively influence the student success prediction task. Experiments on the Open University Learning Analytics (OULA) dataset demonstrate promising results, achieving a 68.6\% validation F1 score with only 7\% of the semester completed, and reaching up to 89.5\% near the semester's end. Our approach outperforms top machine learning models by 4.7\% in validation F1 score during the critical early 7\% of the semester, underscoring the value of dynamic features and heterogeneous graph representations in student success prediction.