Comparing students’ social knowledge construction in the Metaverse-based and chat-based collaborative learning

Abstract

This study investigates how students construct knowledge socially in two contrasting tools, an immersive Metaverse-based platform and a conventional chat-based platform. Grounded in social constructivist theory and employing content analysis and epistemic network analysis (ENA), this study aims to compare interaction patterns and epistemic behaviors that occurred during a structured collaborative learning task focused on cross-cultural issues. A total of 72 undergraduate students from universities in Taiwan and Indonesia participated in small-group tasks using Metaverse and a chat-based platform over a three-week period. Data sources included video-recorded discussions and chat transcripts. Content analysis was conducted using a revised version of the Interaction Analysis Model (IAM), coding discourse into six categories of knowledge construction (KC1–KC6). Additionally, ENA was used to visualize co-occurrence patterns and model the structure of discourse. Quantitative comparisons revealed that students in Metaverse-based learning performed high frequencies of information sharing (KC1) and socially oriented discourse (KC6), whereas students in chat-based learning demonstrated more frequent negotiation of meaning (KC3) and application of knowledge (KC5). ENA visualizations further illustrated stronger epistemic connectivity among advanced discourse moves in the chat-based environment. The findings suggest that Metaverse-based learning is effective for initiating engagement and supporting social presence but may require explicit scaffolding to foster deeper epistemic engagement. In contrast, a chat-based platform supports more reflective and elaborative knowledge construction. These insights highlight the importance of aligning technological affordances with pedagogical design and support the integration of blended modality strategies to optimize collaborative learning outcomes.