Decoding Learner Behavior in Virtual Reality Education: Insights from Epistemic Network Analysis and Differential Sequence Mining

Abstract

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.