Accurate prediction of student performance is crucial for enabling timely interventions and supporting data-driven instructional strategies. Beyond single-term forecasts, it also facilitates early alerts and targeted feedback for learners and instructors. However, many existing models handle spatial relationships and temporal sequences independently, which limits their ability to capture the intricate dependencies embedded in academic data. In this study, we propose a unified spatial-temporal graph aggregation (USTGA) framework that embeds spatial similarity directly into temporal dependency modeling, forming a coherent and dynamically evolving graph representation. Unlike conventional architectures that treat spatial structure as static or auxiliary, the unified graph enables spatial relations to actively modulate temporal information propagation. To further enhance representation learning, an attention-guided aggregation strategy is employed to adaptively highlight informative spatial-temporal neighbors, while stacked aggregation layers progressively refine node representations across multiple scales. By jointly capturing fine-grained spatial-temporal interactions, the proposed framework overcomes limitations inherent in independent modeling strategies. Experiments on an educational dataset demonstrate that USTGA consistently outperforms competitive baselines, achieving reductions in multiple error metrics under repeated evaluations. The results confirm the framework’s effectiveness and robustness, and its design offers high scalability and adaptability, making it well-suited for deployment in diverse educational analytics systems such as early-warning dashboards and program-level advising workflows.
Yu et al. (Tue,) studied this question.