Academic Trajectory Graphs for Temporal Modelling of Student Academic Progression

Accurate prediction of student success is important for improving retention and supporting academic decision-making. This study investigates the use of Graph Neural Networks (GNNs) for modelling academic progression across 13 university faculties, focusing on how structured trajectory representations can capture temporal and relational dependencies in student records. Conventional machine learning approaches frequently rely on aggregated tabular indicators, which may obscure the temporal ordering and relational structure of academic trajectories. We propose a graph-based representation, termed the Academic Trajectory Graph (ATG), which encodes semester-to-semester course transitions and temporal relationships between enrollments. To evaluate its effectiveness, graph-based approaches (DGCNN, GCN, and node2vec-based embeddings) are compared with conventional machine learning models trained on aggregated tabular indicators. Experimental results show that aggregated academic indicators provide strong predictive performance, confirming the importance of cumulative progression signals. Among the graph-based approaches, DGCNN achieved the most consistent performance across faculties, reaching AUC values up to 0.988, accuracy up to 0.945, and F1-scores above 0.94 in several faculties. Although traditional tabular models generally achieved the strongest overall predictive performance, the ATG preserves temporal and relational information that is not captured by flat feature vectors, enabling trajectory-level analysis of academic pathways. The results indicate that graph-based trajectory modelling may provide additional structural insights in faculties exhibiting heterogeneous academic progression patterns, while offering competitive predictive performance. These findings highlight both the potential and the limitations of graph-based approaches for modelling structured educational data, suggesting that their effectiveness depends on the structural diversity of the underlying academic domain.