Harnessing Multiple Intelligences Through Simulation-Based Learning: Enhancing Electric Motor Comprehension in TVET

Abstract

The teaching and learning of electrical machines in Technical and Vocational Education and Training (TVET) face persistent challenges, particularly in helping students grasp complex theoretical concepts and relate them to practical applications. Traditional instructional approaches often fall short in accommodating diverse learning styles, resulting in low engagement and limited conceptual understanding. This study aims to evaluate the effectiveness of a simulation-based learning approach in enhancing students’ comprehension of electric motor operations by aligning instructional design with the theory of multiple intelligences. The research employed a mixed-method action research design involving 30 diploma-level electrical engineering students from Polytechnic Kota Bharu, Malaysia. Content analysis was conducted on students’ pre- and post-test results, survey responses, and classroom observation data collected over a four-week intervention using MATLAB/Simulink and Ansys Maxwell software. Relevant literature from 2020 to 2023 was reviewed to frame the theoretical and empirical context of the study. The findings reveal a significant improvement in students' understanding of motor performance parameters, including torque and efficiency, as well as an enhancement of their analytical and problem-solving skills. Despite initial challenges in software navigation, students reported increased engagement and preference for simulation-based learning over traditional laboratory sessions. The implications of this study are twofold: for educators, it highlights the need to integrate simulation tools systematically to cater to learners with diverse intelligences; for policymakers and industry stakeholders, the study supports the advancement of digital learning strategies to enhance TVET curriculum effectiveness and graduate readiness in a technology-driven workforce. Future integration of simulation-based modules can foster more inclusive, adaptive, and industry-relevant engineering education.

Keywords: simulation-based learning, electric motors, multiple intelligences, MATLAB/Simulink, TVET, engineering education