Special Issue | Predicting students' learning outcomes using machine learning

Publication: Vol. 19, No. 1 (March 2026)

Description

Big data has become a key driver of innovation, gaining increasing attention from researchers, administrators and students. In education, the impact of these new technologies is particularly relevant, as it offers new ways to enhance student learning, improve the professor-student relationship and address pivotal challenges in the current educational systems. The use of big data in education, in consequence, focuses on several key areas (Baig et al., 2020): analyzing student behavior and performance, improving teaching strategies, and integrating data-driven approaches into curricula to create more adaptive learning environments (Musso et al., 2020).

Machine learning (ML) and artificial intelligence (AI) are powerful tools for processing and analyzing large amounts of data and, after the pandemics, the amount of educational data available keeps growing day after day. These technologies have several benefits, and the most important ones are to help identify patterns in student engagement (Khoudi et al., 2025), predict learning outcomes (De la Hoz et al., 2023) and support decision-making in education (Nieto et al., 2019). AI-driven tutoring systems, adaptive learning technologies, and automated assessment tools are transforming traditional education by providing real-time feedback and personalized learning experiences.

This special issue of the Journal of Efficiency and Responsibility in Education and Science (ERIES) seeks high-quality research on the use of Machine Learning/Artificial Intelligence for predicting student learning outcomes. We invite contributions that explore:

  • AI-Driven Tutoring Systems: Implementation of AI-powered tutoring systems to personalize learning and bridge achievement gaps, particularly in post-pandemic education.
  • Educational Data Mining (EDM): Applications of classification, regression, clustering, and association rule mining to predict student performance and optimize learning strategies.
  • Learning Analytics for Performance Prediction: Data-driven approaches to track student engagement, identify at-risk learners, and provide real-time interventions.
  • Adaptive Learning Technologies: ML-driven adaptive learning environments that tailor content and assessments to individual student needs.

This special issue aims to advance research on ML applications in education while ensuring their responsible and effective implementation. We welcome original research and review articles that contribute to this important field.

Objective

To advance research on the use of machine learning, artificial intelligence and big data analytics to improve educational outcomes. Specifically, this special issue explores innovative approaches for predicting student performance, enabling early interventions, and optimizing curriculum design through data-driven decision-making

Instructions

We request that the authors submit their full research papers into the ERIES Journal system. The submitted papers must follow the technical requirements published in the Instructions for authors (please check the journal website) and should be aligned with the special issue objective.

For your inquiries, please contact: andres.acero@tec.mx or martin.flegl@tec.mx.

Guest editors:

  • Andrés Esteban Acero López, PhD., Department of Industrial and Systems Engineering, Tecnologico de Monterrey, Mexico

Timeline

  • 1st call for papers: March 15, 2025
  • Full manuscript submission deadline: September 30, 2025
  • Full manuscript acceptance deadline: February 28, 2026
  • Publication of the special issue: March 31, 2026

References

Baig, M.I., Shuib, L. and Yadegaridehkordi, E. (2020). ‘Big data in education: a state of the art, limitations, and future research directions’, International Journal of Educational Technology in Higher Education, vol. 17, 44. https://doi.org/10.1186/s41239-020-00223-0

De La Hoz, E., Zuluaga, R. and Mendoza, A. (2021) ’Assessing and Classification of Academic Efficiency in Engineering Teaching Programs’, Journal on Efficiency and Responsibility in Education and Science, vol. 14, no. 1, pp. 41–52. https://doi.org/10.7160/eriesj.2021.140104

Musso, M. F., Hernández, C. F. R. and Cascallar, E. C. (2020). ‘Predicting key educational outcomes in academic trajectories: A machine-learning approach’, Higher Education, vol. 80, pp. 875–894. https://doi.org/10.1007/s10734-020-00520-7

Nieto, Y., García-Díaz, V., Montenegro, C., González, C. C. and González Crespo, R. (2019). ‘Usage of machine learning for strategic decision making at higher educational institutions’, IEEE Access, vol. 7, pp. 75007-75017. https://doi.org/10.1109/ACCESS.2019.2919343

Khoudi, Z., Hafidi, N., Nachaoui, M., et al. (2025). New approach to enhancing student performance prediction using machine learning techniques and clickstream data in virtual learning environments. SN Computer Science, vol. 6, 139. https://doi.org/10.1007/s42979-024-03622-6