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

2026-01-11

arXiv.org

AI Summary

This study addresses the challenge of enabling early and continuous prediction of academic success by leveraging multi-source, heterogeneous student data that includes dynamic assessment features and diverse entity relationships. To this end, the authors propose a novel framework that integrates heterogeneous graph neural networks with traditional machine learning methods, uniquely incorporating dynamic assessment features into heterogeneous graph modeling. Complex dependencies among students, courses, and assessments are captured through carefully designed meta-paths. Evaluated on the OULA dataset, the approach achieves a validation F1-score of 68.6% using only 7% of semester data and reaches 89.5% by the semester’s end, outperforming the best conventional model by 4.7% in early prediction performance. These results demonstrate the method’s effectiveness and innovation in modeling temporal and relational student data for academic outcome forecasting.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

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.

Problem

• student success prediction
• heterogeneous graph
• dynamic features
• early intervention
• educational data mining

Innovation

• heterogeneous graph
• deep learning
• dynamic features
• student success prediction
• metapath