A comparative analysis of the automatic modeling of Learning Styles through Machine Learning techniques

Abstract

This Research Full Paper introduces a machine learning methodology to automatically identify the learning style of students interacting with a Learning Management System. Studies in Cognitive Psychology and Pedagogy have already reported that each individual has a specific Learning Style, which describes her/his best means of perceiving and acquiring knowledge. The detection of the personal Learning Style of each student has long been made by using questionnaires; an analysis that demands too much effort, mainly in courses with hundreds of students. Therefore, the automatic modeling of learning styles has gained attention in the computing and education areas. This study compares different Machine Learning algorithms for the detection of students’ Learning Styles. As such, a dataset is extracted from a real course in the Moodle learning platform. This course had 105 students interacting with 252 learning objects during 12 months. The learning styles were described using the classic model of Felder-Silverman. According to the experimental results using these data, a single machine learning algorithm was not able to induce models with predictive accuracy comparable to those from existing alternatives. However, when models from different algorithms were combined, it was possible to obtain a predictive accuracy superior to those reported in the related literature.