DOI: 10.12688/f1000research.182446.1 ISSN: 2046-1402

Metaverse-Based Learning in Higher Education: Transforming Interaction, Engagement, and Knowledge Construction

Yudhi Munadi, Bilqis Salsabila S. Safa, Lamri Lamri
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Background The growing adoption of metaverse technologies in higher education has created new opportunities for immersive and interactive learning experiences. However, limited research has examined the mechanisms through which metaverse-based learning environments influence student engagement, interaction, and knowledge construction. This study investigates the relationships among metaverse experience, social presence, trust, interaction, student engagement, knowledge construction, learning outcomes, and continuous learning intention. The study also proposes and validates the Metaverse Experiential Learning Model (MELM) as a framework for understanding learning processes in immersive virtual environments. Methods A sequential explanatory mixed-methods design was employed. The quantitative phase involved 198 undergraduate students who participated in metaverse-based learning activities and completed a structured questionnaire. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The qualitative phase included semi-structured interviews with 18 purposively selected participants to provide deeper insights into the quantitative findings. Interview data were analyzed using thematic analysis. Results The results revealed that interaction significantly influenced student engagement (β = 0.42, p < 0.001) and knowledge construction (β = 0.28, p < 0.001). Student engagement exerted a strong positive effect on knowledge construction (β = 0.47, p < 0.001) and significantly mediated the relationship between interaction and knowledge construction (β = 0.20, p < 0.001). Metaverse experience significantly affected interaction (β = 0.44, p < 0.001) and student engagement (β = 0.29, p < 0.001), while social presence (β = 0.36, p < 0.001) and trust (β = 0.31, p < 0.001) further enhanced learning engagement and interaction. The structural model demonstrated substantial explanatory power, explaining 67% of the variance in knowledge construction and 64% of the variance in student engagement. Qualitative findings supported these results, highlighting immersive presence, real-time collaboration, shared problem-solving, and reflective engagement as key mechanisms underlying meaningful learning experiences. Conclusions The findings demonstrate that interaction and engagement serve as critical mechanisms linking metaverse experiences to knowledge construction and learning outcomes. The proposed Metaverse Experiential Learning Model (MELM) provides an empirically validated framework for designing immersive learning environments that foster collaboration, engagement, and meaningful learning in higher education.

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