ORIGINAL RESEARCH article
Front. Educ.
Sec. Assessment, Testing and Applied Measurement
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1632315
Machine Learning Models for Academic Performance Prediction: Interpretability and Application in Educational Decision-Making
Provisionally accepted
- 1University of the Americas, Quito, Ecuador
- 2State University of Milagro, Milagro, Guayas, Ecuador
- 3Universidad San Francisco de Quito, Quito, Pichincha, Ecuador
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The integration of artificial intelligence in education has enabled the development of predictive models for academic performance. However, most existing approaches lack interpretability and do not provide actionable insights for decision-making. This study addresses these limitations by deploying optimized machine learning models, specifically XGBoost and Random Forest, to predict student performance considering geographically, institutional, socioeconomic, and academic factors. Unlike previous research focused only on accuracy, this work incorporates SHAP-based interpretability techniques and an interactive decision support system to analyze the impact of various variables on educational outcomes. The model was trained and validated on a dataset of 50,000 student records, optimized through hyperparameter tuning and cross-validation. Results indicate that XGBoost achieves an R² of 0.91, outperforming traditional approaches, and reduces the mean square error (MSE) by 15%. The feature
Keywords: Adaptive Learning, problem-solving, Artificial intelligence in education, Learning personalization, Education. Institutional Use
Received: 28 May 2025; Accepted: 04 Aug 2025.
Copyright: © 2025 Villegas, Guevara-Reyes, Ortiz-Garcés, Andrade and Cox-Riquetti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Iván Ortiz-Garcés, University of the Americas, Quito, Ecuador
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