Dynamic Student Modelling of Learning Styles for Advanced Adaptivity in Learning Management Systems

Learning management systems (LMSs) are commonly used in e-learning; however, they typically do not consider the individual differences of students, including their different background knowledge, cognitive abilities, motivation, and learning styles. A basic requirement for enabling such systems to consider students’ individual characteristics is to know these characteristics first. This paper focuses on the consideration of learning styles and introduces a dynamic student modelling approach that monitors students’ behaviour over time and uses these data to build an accurate student model by frequently refining the information in the student model as well as by responding to changes in students’ learning styles over time. The proposed approach is especially useful for LMSs, which are commonly used by educational institutions for whole programs of study and therefore can monitor students’ behaviour over time, in different courses. The paper demonstrates how this approach can be integrated in an adaptive mechanism that enables LMSs to automatically generate courses that fit students’ learning styles and discusses how dynamic student modelling can help in identifying students’ learning styles more accurately, which enables the LMS to provide more accurate adaptivity and therefore support students’ learning processes more effectively.