Enhancing Computational Thinking through Metaverse-Based Learning: Expert Consensus on Challenges, Pedagogical Strategies, and Future Directions
Keywords:
Computational Thinking (CT); Metaverse; Immersive Learning; Nominal Group Technique (NGT); Spatial-unityAbstract
This study investigates the integration of Computational Thinking (CT) and Metaverse technologies as a means of addressing instructional challenges in Malaysian secondary schools. Specifically, it aims to identify the key barriers in CT instruction and explore how immersive technologies can enhance learning. CT, a vital 21st-century skill, is difficult to teach due to its abstract nature, low student engagement, and infrastructural limitations, particularly in rural areas. Using the Nominal Group Technique (NGT), selected to gain expert consensus on the challenges faced in teaching CT, six experienced computer science educators participated in structured virtual sessions to identify and prioritise key instructional issues. The data were categorised into five principal constructs, including difficulties with abstraction, lack of student motivation, limited problem-solving abilities, absence of real-world applications, and infrastructural constraints. Experts unanimously agreed (100%) that gamified, real-life projects significantly increase student motivation and understanding of CT concepts. The findings suggest that immersive environments utilising Spatial.io and Unity can enhance CT instruction by visualising abstract concepts, promoting engagement, and enabling collaborative, gamified learning experiences. Moreover, the study aligns with Malaysia’s Digital Education Policy (DEP) by addressing the digital divide. A Metaverse-based learning storyboard is proposed to promote CT components such as decomposition, pattern recognition, and algorithmic thinking. The study contributes to the growing body of research on immersive pedagogy and offers a scalable model for CT education using virtual environments, with implications for both educators and policymakers.
https://doi.org/10.26803/ijlter.24.5.5
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