Identifying Learning Styles in Learning Management Systems by Using Indications from Students' Behaviour

TLDR

Making students aware of their learning styles and presenting them with learning material that incorporates their individual learning styles has potential to make learning easier for students and increase their learning progress. The study proposes an automatic method to infer students’ Felder‑Silverman learning styles from their online learning behavior. The method infers learning styles from online behavior, was implemented in learning management systems, and was evaluated with 127 students by comparing its results to a questionnaire. The evaluation with 127 students showed that the automatic approach accurately identifies learning styles and can support students by tailoring instruction to their individual styles.

Abstract

Making students aware of their learning styles and presenting them with learning material that incorporates their individual learning styles has potential to make learning easier for students and increase their learning progress. This paper proposes an automatic approach for identifying learning styles with respect to the Felder-Silverman learning style model by inferring their learning styles from their behaviour during they are learning in an online course. The approach was developed for learning management systems, which are commonly used in e-learning. In order to evaluate the proposed approach, a study with 127 students was performed, comparing the results of the automatic approach with those of a learning style questionnaire. The evaluation yielded good results and demonstrated that the proposed approach is suitable for identifying learning styles. By using the proposed approach, studentspsila learning styles can be identified automatically and be used for supporting students by considering their individual learning styles.