Improving Digital Learning Experiences in TVET: A Computer-Aided Emotion Assessment Using LEIQ™

Abstract

Emotional engagement plays a crucial role in shaping learning experiences, particularly in Technical and Vocational Education and Training (TVET), where practical and applied skills require high levels of user interaction. Integrating emotional responses into user experience (UX) evaluation in the digital era is essential for improving web-based learning environments. This research introduces a computer-aided assistive tool to enhance emotional UX assessment using Lokman’s Emotions and Importance Quadrant (LEIQ™) model. By automating data collection and processing, the tool provides a systematic and scalable approach to understanding learners' emotional responses. Existing emotion measurement tools struggle to handle large datasets, often relying on manual processes that limit efficiency and accuracy. To address these limitations, this paper presents the design, development, and evaluation of an automated LEIQ™-based assistive tool, comparing its effectiveness with traditional manual methods through qualitative analysis and user testing. Findings indicate that the tool significantly enhances the accuracy and efficiency of emotion assessment, providing valuable insights for educators, instructional designers, and researchers in the education sector. This advancement has broader implications for UX practitioners, technology developers, and researchers, offering a scalable solution for integrating emotional intelligence into digital learning environments. By refining the LEIQ™ assessment process, this research contributes to the development of more engaging, responsive, and effective educational tools, ultimately enhancing learning outcomes in vocational and technical education.