Effective Blended Learning Model Selection Based on Student Learning Style using Analytic Hierarchy Process for an Undergraduate Engineering Course

Abstract

Blended learning is a flexible method conducted through face-to-face and online learning. It requires students to learn by attempting the classes physically and allows them to learn virtually at different times and places. It has become more evident and common after the Movement Control Order (MCO) as most of the lectures at the university are carried out in hybrid mode. The blended learning models create problems and opportunities for students as they need to explore and adapt to different lecturers' different blended learning methods in terms of teaching styles, planning, and timing. Therefore, the objective of this research is to investigate the best-blended learning models for an undergraduate engineering course based on student learning style by using the Analytic Hierarchy Process (AHP) method, as it is a big challenge to select the most effective approach for universities to educate, tutor and bring out quality students according to their learning styles. The AHP method is used to aid the students in finding the best-blended learning model based on their learning style. AHP analysis is then conducted to validate and verify its accuracy by comparing it with Visual, Auditory, Read/write, and Kinesthetic (VARK) models. As a result, most students are kinaesthetic learners (72%) based on VARK results, and the face-to-face driver model is the most preferred blended learning model with the priority vector at 31.33% through the AHP analysis. The accuracy of the AHP result is 74% by comparing it with the VARK result. In summary, the data can be deployed in the UTeM blended learning system to improve the course design and student learning experience.