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.

翻译：早期识别学生学业成功对于实施及时干预、降低辍学率以及促进按时毕业至关重要。在教育场景中，凭借其先进的分析能力，人工智能驱动的系统已成为预测学生学业表现的重要工具。然而，如何有效利用多样化的学生数据来揭示潜在且复杂的模式，仍然是一个关键挑战。尽管先前的研究已在此领域有所探索，但动态数据特征与多类别实体的潜力在很大程度上被忽视了。为弥补这一不足，我们提出了一个集成异质图深度学习模型的框架，以增强早期且持续的学生学业表现预测，并使用传统机器学习算法进行对比。我们的方法采用图元路径结构，并融入动态评估特征，这些特征会逐步影响学生学业成功的预测任务。在开放大学学习分析数据集上的实验展示了有前景的结果：在学期仅完成7%时即获得68.6%的验证F1分数，并在学期末达到最高89.5%。在学期关键的早期7%阶段，我们的方法在验证F1分数上优于顶尖机器学习模型4.7%，这凸显了动态特征与异质图表征在学生学业成功预测中的价值。