Developing a Multimodal Learning Analytics Approach to Examine Students' Cognitive Presence and Metacognition in a Metaverse Environment

Abstract

The rise of the metaverse as an educational platform has introduced new opportunities and challenges in understanding students' cognitive processes. Traditional learning analytics methods often fail to fully capture the dynamic patterns of cognitive presence and metacognitive activities in immersive virtual environments, particularly when students interact with an artificial intelligence (AI)-powered digital human. To address this gap, this study aims to develop a multimodal learning analytics (MMLA) approach to analyse cognitive presence and metacognition in Learningverse - a metaverse platform. To address the limitations of traditional methods, this research integrated eye-tracking data with dialogue text from the AI-powered digital human to provide a more comprehensive understanding of these patterns. A pilot study involving undergraduate students was conducted to collect data, revealing specific patterns of cognitive presence and metacognition. The findings suggest that the MMLA approach could offer deeper insights into students' learning behaviours, providing valuable implications for the design of educational tools and the development of more effective learning strategies in virtual environments.