Automatic Student Modelling for Detection of Learning Styles and Affective States in Web Based Learning Management Systems

Abstract

In traditional learning environments, it is easy for a teacher to get an accurate and deep understanding about how students are learning and undertaking tasks. This results in teacher understanding about each student’s learning preferences and behavior, which exhibits, for instance, students’ learning styles and affective states. On the other hand, identification of learning styles and affective states in web-based learning environments is quite challenging. The existing approaches for the identification of learning styles such as questionnaire is not without limitations. Similarly, affective states identification approaches mentioned in the literature indicate several shortcomings. This paper proposes an automatic approach for the identification of learning styles and affective states in web-based Learning Management Systems (LMSs). The unique feature of this approach is that it is generic in nature. Using this approach, the students learning styles and affective states are calculated automatically from their learning preferences and behavior within a course. Evaluation of this approach was performed by following a study with 81 students. The results of the study were then compared with the learning styles and affective states questionnaires, which demonstrate that the suggested approach is more appropriate for the identification of learning styles and affective states. Therefore, using this approach, a tool (AsLim) has been developed and can be used by the teachers for the identification of learning styles and affective states of their students.

Keywords

• Learning management systems (LMSs)
• learning styles
• affective states
• adaptivity
• personalization