Predicting student academic performance using TabPFN and SHAP: An interpretable machine learning approach
- 1School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong, China
Abstract
Predicting student academic performance is of significant importance for enabling timely educational interventions and improving educational outcomes in secondary education. However, existing predictive models in educational data mining often face the dual challenges of limited interpretability and suboptimal performance on small to medium-sized datasets. This study introduces the Prior-Data Fitted Network (TabPFN) combined with SHapley Additive exPlanations (SHAP) to construct an interpretable machine learning framework for predicting student achievement. Utilizing a publicly available dataset of 4,000 Spanish high school students (SAP-4000), we employed 10-fold cross-validation to train and compare eight machine learning models. The results demonstrate that TabPFN achieves superior predictive performance, with an RMSE of 4.8454, an R2 of 0.9216, and an MAE of 3.7950, outperforming traditional models. Comparative analyses across varying data scales indicate that TabPFN maintains high accuracy even when sample sizes are reduced to 500 samples, achieving performance comparable to or better than what traditional models attained at 3,500 samples, exhibiting stronger robustness than commonly used ensemble learning methods such as XGBoost. SHAP analysis reveals that learning behavior and study habit features—specifically weekly study hours, attendance rate, and tutoring status—play a decisive role in predicting academic performance. The proposed framework combines strong predictive performance with interpretability, overcoming the “black-box” limitation prevalent in existing algorithms. It is well-suited for small to medium-scale data modeling tasks in the educational domain, providing technical support for personalized instruction and intervention, while also offering methodological references for future research. An additional case study on a distinct student performance dataset further confirms the model’s robustness and generalizability across different educational prediction tasks.
Related
- An interpretable machine learning model for predicting response to anti-GD2 combined with umbilical cord blood-derived natural killer cell immunotherapy in neuroblastoma
- Interpretable metaheuristic-optimized machine learning for predicting biodiversity indices in the Hyrcanian forest
- Academic entitlement and student well-being: The mediating role of the impostor phenomenon
- MF-TLMLP: A multi-channel fusion-based multi-fidelity transfer learning method for predicting the cutting performance of conical picks
- Predicting Elephant Crop-Raiding with Machine Learning to Inform Conservation Planning: A Case Study Comparing the Performance and Tradeoffs of GeoAI Algorithms in Northwestern Zimbabwe
BibTeX
@article{yin2026predicting,
title = {{Predicting student academic performance using TabPFN and SHAP: An interpretable machine learning approach}},
author = {Qiang Yin},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0328634},
url = {https://doi.org/10.1371/journal.pone.0328634},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predicting student academic performance using TabPFN and SHAP: An interpretable machine learning approach
AU - Qiang Yin
PY - 2026
DA - 2026/09/24
JO - PLOS One
DO - 10.1371/journal.pone.0328634
UR - https://doi.org/10.1371/journal.pone.0328634
AB - Predicting student academic performance is of significant importance for enabling timely educational interventions and improving educational outcomes in secondary education. However, existing predictive models in educational data mining often face the dual challenges of limited interpretability and suboptimal performance on small to medium-sized datasets. This study introduces the Prior-Data Fitted Network (TabPFN) combined with SHapley Additive exPlanations (SHAP) to construct an interpretable machine learning framework for predicting student achievement. Utilizing a publicly available dataset of 4,000 Spanish high school students (SAP-4000), we employed 10-fold cross-validation to train and compare eight machine learning models. The results demonstrate that TabPFN achieves superior predictive performance, with an RMSE of 4.8454, an R2 of 0.9216, and an MAE of 3.7950, outperforming traditional models. Comparative analyses across varying data scales indicate that TabPFN maintains high accuracy even when sample sizes are reduced to 500 samples, achieving performance comparable to or better than what traditional models attained at 3,500 samples, exhibiting stronger robustness than commonly used ensemble learning methods such as XGBoost. SHAP analysis reveals that learning behavior and study habit features—specifically weekly study hours, attendance rate, and tutoring status—play a decisive role in predicting academic performance. The proposed framework combines strong predictive performance with interpretability, overcoming the “black-box” limitation prevalent in existing algorithms. It is well-suited for small to medium-scale data modeling tasks in the educational domain, providing technical support for personalized instruction and intervention, while also offering methodological references for future research. An additional case study on a distinct student performance dataset further confirms the model’s robustness and generalizability across different educational prediction tasks.
SN - 1932-6203
ER -