Unveiling Learners’ Interaction Behavior in Virtual Reality Learning Environment

Abstract

VR is increasingly being utilized in various domains, including education, due to its unique characteristics. Research in this area often relies on physiological sensors, eye-trackers, VR device orientation, human observers, and pre-test and post-test to collect data for quantitative studies and on questionnaires, surveys, and interviews to collect data for qualitative studies in VR Learning. However, there is a dearth of reliable data sources for studying learner behavior in VRLE, and minimal efforts have been made to automatically collect behavioral data in this context. Furthermore, there is a lack of studies that investigate learning processes through the lens of learners' dynamic interaction behavior in VRLE. To address these gaps, we have developed a real-time data collection mechanism that automatically logs learners' interaction behavior in VRLE, including timestamps. This mechanism was deployed in a room-scale VRLE called MaroonVR, and a study was conducted involving undergraduate engineering students. The main objectives of this paper are to identify differences in interaction behavior between high and low performers and to develop an optimal predictor model to predict the learning outcome using learners' interaction behavior in VRLE. Furthermore, we propose that the study's findings can be utilized to model learners' behavior in VR and to provide scaffolding and adaptive personalized VR learning content.