The integration of immersive Virtual Reality (I-VR) technology in education has emerged as a promising approach for enhancing learning experiences. There is a handful of research done to study the impact of I-VR on learning outcomes, comparison of learning using I-VR and other traditional learning methods, and the impact of values such as haptic sensation, and verbal and non-verbal cues on the learning outcomes. However, there is a dearth of research on understanding how learning is happening from the perspective of the behavior of the learners in the Virtual Reality Learning Environment (VRLE). To address this gap, we developed an Interaction Behavioral Data (IBD) logging mechanism to log all the interaction traces that constitute the behavior of the learners in a Virtual Reality Learning Environment (VRLE). We deployed the IBD logging mechanism in a VRLE used to learn electromagnetic induction concepts and conducted a study with 30 undergraduate computer science students. We extract the learners' actions from the logged data and contextualize them based on the action features such as duration (Long and Short), and frequency of occurrence (First and Repeated occurrence). In this paper, we investigate the actions extracted from logged interaction trace data to understand the behaviors that lead to high and low performance in the VRLE. Using Epistemic Network Analysis (ENA), we identify differences in prominent actions and co-occurring actions between high and low performers. Additionally, we apply Differential Sequence Mining (DSM) to uncover significant action patterns, involving multiple actions, that are differentially frequent between these two groups. Our findings demonstrate that high performers engage in structured, iterative patterns of experimentation and evaluation, while low performers exhibit less focused exploration patterns. The insights gained from ENA and DSM highlight the behavioral variations between high and low performers in the VRLE, providing valuable information for enhancing learning experiences in VRLEs. These insights gained can be further utilized by the VR content developers to develop adaptive VR learning content by providing personalized scaffolding leading to the enhancement in the learning process via I-VR.

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.

Embodied Cognition theory asserts that the physical body, its environment, and the interplay between them hold a pivotal role in the process of embodied learning. In comparison to traditional and computer-based learning methods, Virtual Reality (VR) enhances embodied learning, primarily owing to its capacity to offer a sense of situatedness and engage learners through physical interactions. Furthermore, the incorporation of haptic sensations in VR-based learning introduces tactile sensory memory, complementing the existing auditory and visual sensory dimensions. Despite the evident advantages of VR in facilitating embodied learning, the current body of literature has yet to delve into the dynamic behaviors exhibited by learners within Virtual Reality Learning Environments (VRLEs) during embodied interactions, and their inherent relationship with the embodiment phenomenon. In this paper, we take a significant step by integrating the Interaction Behavioral Data (IBD) collection mechanism, a development from our previous work, into a VRLE. This integration is achieved by adopting a structured framework for embodied learning activities within a VR context. Through an empirical study involving 14 participants, we meticulously logged their interaction traces using the IBD logger. Subsequently, we effectively extracted the diverse embodied learning activities carried out by these participants within the VRLE, from the interaction trace data collected in the IBD logger. Our endeavor aims to establish meaningful connections between these embodied interaction activities and the overarching concept of embodiment. These correlations, once identified, will serve as invaluable insights for VR content developers, offering clear guidelines for the design and creation of VRLEs that optimally facilitate enhanced learning experiences. Ultimately, this knowledge transfer will not only empower instructors and learners to harness VR as a potent educational tool within their regular teaching and learning practices but also foster the seamless integration of VR into mainstream education.

Virtual Reality (VR) has the potential to improve learning in the education domain due to its characteristics such as multi-sensory stimuli, immersion, interaction, first-person perspectives, etc. In spite of these advantages, the literature analysis carried out has revealed that there is no literature that explored the learning processes happening in a VR environment. The learning processes can be analyzed using the data collected from the interaction behavior happening in a VR learning environment (VRLE). The existing data collection mechanisms in VR such as questionnaires, surveys, physiological sensors, and human observers do not provide data related to the interaction behavior of the learners happening in VR head-mounted displays (HMD). Hence, in this paper, we discuss a mechanism developed to log the interaction data happening in VRLE automatically in real-time and store them in a locally accessible location. The logged interaction data can be further processed to extract features and study the learning processes in VRLE.