Supporting Teachers in Identifying Students' Learning Styles in Learning Management Systems: An Automatic Student Modelling Approach

TLDR

In LMSs, teachers struggle to detect individual student behavior, and understanding learning styles can improve teacher support and learning outcomes. This study proposes an automatic method and accompanying tool to identify students’ learning styles within LMSs. The method infers styles from students’ online activity and was evaluated with 127 participants against a questionnaire. The evaluation showed the method accurately identifies learning styles, enabling teachers to tailor support to individual students.

Abstract

In learning management systems (LMSs), teachers have more difficulties to notice and know how individual students behave and learn in a course, compared to face-to-face education. Enabling teachers to know their students’ learning styles and making students aware of their own learning styles increases teachers’ and students’ understanding about the students’ learning process, allows teachers to provide better support for their students, and has therefore high potential to enhance teaching and learning. This paper proposes an automatic approach for identifying students’ learning styles in LMSs as well as a tool that supports teachers in applying this approach. The approach is based on inferring students’ learning styles from their behaviour in an online course and was developed for LMSs in general. It has been evaluated by a study with 127 students, 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. DeLeS, the tool which implements this approach, can be used by teachers to identify students’ learning styles and therefore to support students by considering their individual learning styles.