Your new experience awaits. Try the new design now and help us make it even better

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
William  VillegasWilliam Villegas1Rodrigo  Guevara-ReyesRodrigo Guevara-Reyes2Iván  Ortiz-GarcésIván Ortiz-Garcés1*Roberto  AndradeRoberto Andrade3Fernanda  Cox-RiquettiFernanda Cox-Riquetti1
  • 1University of the Americas, Quito, Ecuador
  • 2State University of Milagro, Milagro, Guayas, Ecuador
  • 3Universidad San Francisco de Quito, Quito, Pichincha, Ecuador

The final, formatted version of the article will be published soon.

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

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.