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.
Patel et al. (Mon,) studied this question.