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Application of explainable artificial intelligence approach to predict student learning outcomes

Author

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  • Jean-Baptiste M.B. SANFO

    (The University of Shiga Prefecture)

Abstract

Machine learning can help predict critical educational outcomes, but its “black-box” nature is a significant challenge for its broad adoption in educational settings. This study employs a variety of supervised learning algorithms applied to data from Burkina Faso’s 2019 Program for the Analysis of CONFEMEN Education Systems. Shapley Additive Explanation (SHAP) is then used on the selected algorithms to identify the most significant factors influencing student learning outcomes. The objectives of the study are to (1) to apply and evaluate supervised learning models (classification and regression) to achieve the highest performance in predicting student learning outcomes; (2) to apply Shapley Additive Explanation (SHAP) to extract the features with the highest predictive power of students’ learning outcomes. Results showed that K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) have the best predictive power for classification tasks. Likewise, the Random Forest Regressor showed the best predictive accuracy for the regression task. SHAP values were then utilized to determine feature contribution to predictions. The key predictive features identified are “local development,” “community involvement,” “school infrastructure,” and “teacher years of experience.” These findings suggest that learning outcomes are significantly influenced by community and infrastructural factors and teacher experience. The implications of this study are substantial for educational policymakers and practitioners. Emphasizing “local development” and “community involvement” underscores the necessity of community engagement programs and partnerships. Prioritizing investments in school infrastructure can enhance the learning environment, while recognizing the impact of teacher years of experience highlights the need for professional development and retention strategies for educators. These insights advocate for a comprehensive approach to improving educational outcomes through targeted investments and strategic community collaborations.

Suggested Citation

  • Jean-Baptiste M.B. SANFO, 2025. "Application of explainable artificial intelligence approach to predict student learning outcomes," Journal of Computational Social Science, Springer, vol. 8(1), pages 1-33, February.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:1:d:10.1007_s42001-024-00344-w
    DOI: 10.1007/s42001-024-00344-w
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    References listed on IDEAS

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    1. Belmonte, Alessandro & Bove, Vincenzo & D’Inverno, Giovanna & Modica, Marco, 2020. "School infrastructure spending and educational outcomes: Evidence from the 2012 earthquake in Northern Italy," Economics of Education Review, Elsevier, vol. 75(C).
    2. Sahar Saeed Rezk & Kamal Samy Selim, 2024. "Comparing nine machine learning classifiers for school-dropouts using a revised performance measure," Journal of Computational Social Science, Springer, vol. 7(2), pages 1555-1597, October.
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