Predicting Students’ Learning Stylesusing Machine Learn‎ing

  • Authors

    https://doi.org/10.14419/xv1tn693

    Received date: January 12, 2026

    Accepted date: January 12, 2026

    Published date: February 23, 2026

  • Artificial Intelligence; Learning; Learning Style; Machine Learning Models; Students Success

  • Abstract

    Understanding Learning Styles (LS) of students in the times of Artificial Intelligence (AI) can be beneficial for students’ success. Current ‎study investigates the applicability of several supervised Machine Learning (ML) models to envisage students' learning styles based on ‎demographic and behavioral characteristics. The data, collected from both engineering and non-engineering students viz. commerce, science, ‎and arts, includes male and female students in the age range of 17 to 30 years. CGPA (on 10-point scale), study hours (weekly), and pre-‎ferred study times (viz. morning, afternoon, evening, and late night), were considered as the key attributes of research. This research at-‎tempts to discover the abilities of ML classifiers for precise prediction of student's learning style from diverse and multi-dimensional data. ‎Further analysis is done to identify the best performing algorithms aiming towards the development of adaptive learning technologies in ‎educational settings‎.

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  • How to Cite

    Patel, D. N., & Sable, R. G. . (2026). Predicting Students’ Learning Stylesusing Machine Learn‎ing. International Journal of Accounting and Economics Studies, 13(2), 312-324. https://doi.org/10.14419/xv1tn693