Learning Behavior Analysis to Identify Learner's Learning Style based on Machine Learning Techniques

Abstract

Learning styles cover various attributes related to the attitude and the learning behavior of individuals. Research and educational theories confirm that considering learning styles in distance learning environments can improve academic performance and learner satisfaction. The traditional approach to identify learning styles is based on asking students to fill out a questionnaire. This approach is considerably less accurate due to the learners’ lack of awareness of their own preferences. Furthermore, learners’ learning styles are defined only once. In this study, we propose an automatic approach to identify learners’ learning styles based on patterns of learning behavior with respect to Felder and Silverman Learning Style Model (FSLSM), in an online learning environment. Patterns of behavior were analysed based on a data-driven approach. This approach exploits different Machine Learning (ML) techniques to detect the learning styles of learners. To validate our proposals, experiments were carried out in a higher education institution with 73 students enrolled in online courses on the ADLS (Automatic Detection of Learning Styles) system that we implemented. A 9 runs cross-validation was used to evaluate the selected ML techniques. Detection accuracy, recall, precision, and F measure were observed. The findings show the possibility of detecting learning styles automatically based on learning behavior with high performances. Different levels of accuracy were found for the different dimensions of FSLSM. However, Support Vector Machines (SVM) have exhibited great ability in predicting learning styles for all dimensions of FSLSM with an accuracy average of 88%.