Effective prediction of academic risk is vital in higher education to enable timely intervention and support student retention. While the introduction of Educational Data Mining (EDM) has enhanced prediction effectiveness, existing research often focuses only on single factors or large scale samples, and is notably deficient in providing transparent explanations for prediction results. To address these gaps, this study proposes an Explainable Artificial Intelligence (XAI) framework for predicting and interpreting academic risk within a high-dimensional, small sample context. Based on a dataset from a specific student cohort, we employed an ML model combined with SHapley Additive exPlanations (SHAP) method as the XAI framework. The findings provide two major contributions to the “Data-Related Challenges in ML” discussion. Firstly, by leveraging the XAI framework, it successfully enhances data interpretability, revealing the out-of-class peer support as the feature with the strongest association with academic risk, which is a complex and often underestimated data dimension, surpassing traditional academic metrics. Specifically, learning support from peers is identified as the most critical feature in mitigating risk at both the group and individual levels. Secondly, methodologically, this framework validates a reliable approach for extracting meaningful, trustworthy, and interpretable knowledge from limited and specific cohort data, offering a solution for applications with highly contextualized and precise interventions, where large, generalizable datasets are impractical. In conclusion, this study enhances the transparency and trustworthiness of ML in EDM, ensuring responsible intervention strategies in academic risk prediction.
Sun et al. (2026) studied this question.