Towards efficient student performance prediction with tabular prior-data fitted network: a small-sample learning approach in data-scarce scenarios

Accurate prediction of student academic performance is crucial for enhancing educational outcomes and enabling personalized learning interventions. However, the limited availability of labeled data in many practical educational settings poses significant challenges for conventional machine learning models, which often require large datasets to achieve satisfactory performance and are prone to overfitting. To address this issue, we propose a novel small-sample learning framework based on the Tabular Prior-Data Fitted Network (TabPFN). By integrating a Transformer based architecture with Bayesian inference, TabPFN effectively leverages meta-learned priors to perform high-quality predictions without task-specific finetuning. We evaluated the approach on the Mathematics and Portuguese subsets of the UCI Student Performance dataset and on a pooled Combined dataset under small-sample conditions. Experimental results indicate that TabPFN achieved generally strong and competitive performance across the three datasets, with accuracies of 92.36%, 95.49%, and 94.29%, respectively. However, its advantage varied across datasets and metrics, and the results should be interpreted cautiously where confidence intervals overlapped with strong baseline models. Additionally, we employ Shapley Additive Explanations (SHAP) to interpret model decisions, identifying key influencing factors such as prior grades (G1, G2) and prior educational experience. The findings indicate that TabPFN constitutes a promising and effective solution for educational prediction in data scarce scenarios but also provides actionable insights for instructional design and student support.