A Comparative Analysis of Machine Learning Models for Predicting EFL Student Language Performance in Smart Learning Environments

Banchakarn Sameephet; Wirapong Chansanam; Mahboubeh Rakhshandehroo; Chawin Srisawat; Kittichai Nilubol; Arnon Jannok; Bhirawit Satthamnuwong; Kornwipa Poonpon

doi:10.28991/ESJ-2025-09-02-07

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Banchakarn Sameephet Smart Learning Innovation Research Center and Faculty of Humanities and Social Sciences, Khon Kaen University, Khon Kaen,, Thailand
Wirapong Chansanam
wirach@kku.ac.th
Smart Learning Innovation Research Center and Faculty of Humanities and Social Sciences, Khon Kaen University, Khon Kaen,, Thailand http://orcid.org/0000-0001-5546-8485
Mahboubeh Rakhshandehroo Faculty of Culture and Representation, Doshisha Women's College of Liberal Arts, Kyoto, Japan; Center for Multilingual Education, Osaka University, Osaka,, Japan
Chawin Srisawat Smart Learning Innovation Research Center and Faculty of Humanities and Social Sciences, Khon Kaen University, Khon Kaen,, Thailand
Kittichai Nilubol Smart Learning Innovation Research Center and Faculty of Humanities and Social Sciences, Khon Kaen University, Khon Kaen,, Thailand
Arnon Jannok Smart Learning Innovation Research Center and Faculty of Humanities and Social Sciences, Khon Kaen University, Khon Kaen,, Thailand
Bhirawit Satthamnuwong Smart Learning Innovation Research Center and Faculty of Humanities and Social Sciences, Khon Kaen University, Khon Kaen,, Thailand
Kornwipa Poonpon Smart Learning Innovation Research Center and Faculty of Humanities and Social Sciences, Khon Kaen University, Khon Kaen,, Thailand

Vol. 9 No. 2 (2025): April

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Integrating smart learning environments into modern education systems opens up significant opportunities to use data analysis techniques to predict students' English language performance. This study aims to evaluate the performance of various machine learning models for predicting English as a foreign language student performance, emphasizing data preprocessing and feature selection. The dataset was gathered from 181 students in eight middle schools in Thailand. The student's data was exported from the Smart Learning Project, which includes data on 14 PISA-like English quizzes covering 27 competencies. The study compares the predictive performance of machine learning models, including Random Forest, Support Vector Regression, AdaBoost, Bayesian Ridge, K-Nearest Neighbors, ElasticNet, XGBoost, Gradient Boosting, and Stacking Ensemble, using MSE, RMSE, MAE, and R² metrics. The analysis results indicated that ensemble models, particularly XGBoost and Stacking Ensemble, performed the best in predicting students' English language performance. These models can efficiently capture complex relationships in educational data. Therefore, data preprocessing and feature selection play a significant role in improving model performance. This study highlights the potential of advanced machine learning techniques in educational data analysis. The results can contribute to developing personalized learning strategies and early intervention. It supports an efficient and adaptive education system, advancing smart learning and data-driven instruction.

Doi: 10.28991/ESJ-2025-09-02-07

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Sameephet, B., Chansanam, W., Rakhshandehroo, M., Srisawat, C., Nilubol, K., Jannok, A., … Poonpon, K. (2025). A Comparative Analysis of Machine Learning Models for Predicting EFL Student Language Performance in Smart Learning Environments. Emerging Science Journal, 9(2), 615–639. https://doi.org/10.28991/ESJ-2025-09-02-07

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A Comparative Analysis of Machine Learning Models for Predicting EFL Student Language Performance in Smart Learning Environments

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Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

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Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites