A Web-Based Computerized Adaptive Testing with Rasch Analysis for Identifying VAK Learning Styles

Understanding learner variability in learning styles is critical for optimizing personalized learning benefits. Learning style, a concept within educational psychology, has been explored through various theoretical frameworks, including VAK (Visual, Auditory, and Kinesthetic) learning modalities. This study utilized a multidisciplinary approach to develop and test a web-based Computerized Adaptive Testing (CAT) system, aiming to overcome the limitations of conventional questionnaire-based assessments, such as potential disinterest and unreliable outcomes, in determining VAK learning styles. The study approach involved the recruitment of computer science students from Mae Fah Luang University. It also included developing a web-based CAT system using Django. Furthermore, an item bank based on Revised Bloom's Taxonomy was created. Additionally, the validity of the instruments used was evaluated using the Rasch model. We conducted an analysis of User Experience Questionnaire (UEQ) data to evaluate the overall user experience of both the conventional and CAT tests, and the statistics indicate that the web-based CAT offers a more user-friendly experience. The study also utilized the concordance rate to gauge the similarity of data responses among the conventional, CAT, and self-assessment tests concerning VAK learning styles. The findings indicate that the proposed method exhibits an 87% similarity with self-assessment and shows significant potential.