Learning Analytics for the Metaverse: A Review of Descriptive Analytics and Visualization Tools for Metaverse-Based Learning Environments

Abstract

The adoption of metaverse technology in education offers new possibilities for experiential, collaborative, and immersive learning, while simultaneously generating complex, multimodal learner data. Interpreting this dense information, spanning spatial navigation, object manipulation, avatar embodiment, and communication patterns poses a significant challenge for both educators and learners. This review synthesizes 19 peer-reviewed studies identified through systematic database searches and snowballing, focusing on the current state of data visualization and descriptive learning analytics in metaverse-based Learning Environments. The analysis reveals that while platforms like Learningverse and tools such as PLUME enable non-interactive or post-hoc data exploration, there is a lack of real-time, pedagogically integrated visualization systems. Current approaches are largely descriptive, fragmented, and disconnected from immediate instructional support or learner reflection. This review highlights critical research gaps, including the absence of runtime interactive tools, underutilization of immersive analytics, and limited alignment with educational theory. Addressing these gaps is essential to transforming rich multimodal data into actionable insights that can enhance teaching and learning within the metaverse.