Fostering motivation in TVET students: the role of learner-paced segments and computational thinking in digital video learning

Abstract

In recent years, digital video courseware has emerged as an effective tool for enhancing learning experiences, particularly in Technical and Vocational Education and Training (TVET) programs. As technological advancements continue to reshape industries, there is an increasing demand for students to develop both digital competence and problem-solving abilities, such as Computational Thinking (CT). Despite its importance, the integration of CT principles with personalized, self-paced learning approaches, such as learner-paced digital video courseware, has not been extensively explored. This study aims to address this gap by examining how learner-paced predefined segments and CT algorithmic thinking can impact TVET students' perceived motivation. This quasi-experimental study compares two courseware modes: learner-paced and system-paced predefined segments, to evaluate their effects on student motivation. Content delivered in the learner-paced mode allows students to progress through segments at their own pace, while the system-paced mode follows a fixed sequence. The findings from ANOVA analysis revealed that students in the learner-paced mode exhibited significantly higher motivation levels than those in the system-paced mode. These results highlight the benefits of integrating self-paced learning with CT strategies, suggesting that such instructional designs can enhance motivation and engagement in TVET education. The study advocates for the adoption of these approaches to improve student outcomes and better equip them with the skills needed for the digital workforce.