Jeeheon Ryu

This study investigates the critical factors influencing learning experiences within Extended Reality (XR) simulation environments, bridging the gap between advanced technology and pedagogical practice. Utilizing a mixed-methods approach, a single-group pretest–posttest study was conducted with undergraduate students using a geography-themed XR simulation grounded in Learning Experience Design (LXD) principles. Quantitative measures assessed changes in academic self-efficacy and achievement, while post-test instruments captured perceived attention and learning satisfaction. Semi-structured interviews with a subsample of participants provided qualitative insights into their embodied learning experiences, analyzed through a stepwise coding process. Participants showed statistically significant pre-to-post increases in both academic self-efficacy and achievement, alongside high levels of attention and satisfaction. Qualitative analysis identified specific affordances and constraints that participants associated with their engagement and performance; conceptual reorganization through physical action offered the most coherent interpretation of this pattern, although the proposed mechanism was not tested directly. The findings point to four design considerations for XR learning environments: direct manipulation, repeatable practice, learner-controlled scaffolded feedback, and ergonomically considerate interfaces. This study offers design guidance for XR-based educational applications, to be interpreted within the constraints of a single-group, single-session design.

This study examined how task complexity and self-regulated learning (SRL) support strategies relate to learners’ cognitive load and emotional engagement in a Metaverse Learning Environment (MLE) using multimodal indicators. College students ( N = 42) completed three game-embedded tasks of low, mid, and high complexity while receiving cognitive-regulated support (CS), motivational-regulated support (MS), or no additional support (control group, CG). Learners’ cognitive and emotional engagement were assessed using self-reported mental effort and multimodal measures for process approaches, an EEG-derived mental workload index (beta/[alpha + theta]), frontal alpha asymmetry (FAA), and facial expression analysis (FEA) metrics of task-facing orientation and engagement. Task complexity showed robust but non-monotonic effects across modalities. The mid-complexity task elicited higher EEG workload and emotional engagement than the low- and high-complexity tasks, whereas task-facing orientation was lower in the high-complexity task than in the low- and mid-complexity tasks. FAA was also higher in the mid-complexity task than in the high-complexity task. SRL support strategies were modality-specific: the control group reported higher mental effort than the CS group, and the CS group showed higher expressive engagement than both the MS and CG groups during the mid-complexity task. These findings suggest that cognitive support may reduce subjective strain while sustaining emotional involvement under moderate challenge. More broadly, the results highlight the value of multimodal measurement for distinguishing perceived effort from physiological and behavioral indicators of learner experience in immersive learning environments.

Classroom management is an essential yet frequently under-practiced competency in undergraduate teacher education, with important implications for sustainable teacher preparation. This study investigated whether pre-service teachers who feel more capable of managing classrooms also engage more deeply with simulation-based training. Fifty-seven Korean pre-service teachers (15 men, 42 women), all undergraduate students enrolled in a secondary teacher education program at a college of education, completed a five-item classroom-management-efficacy scale, then experienced a 15 min branching simulation that required choosing recognition, punishment, or aggression strategies in response to a disrespectful virtual student. Interest was assessed immediately afterwards with a 24-item instrument covering the four phases of the interest-development model (triggered situational, maintained situational, emerging individual, and well-developed individual). A post-test comparative design and MANOVA revealed that efficacy level had a significant multivariate effect on overall interest (Wilks Λ = 0.78, p = 0.029, partial η² = 0.12). Scheffe contrasts showed that high-efficacy participants outscored their low-efficacy peers on maintained situational and emerging individual interest, p < 0.05, and surpassed the middle-efficacy group in three of the four phases. Repeated measures ANOVA confirmed a general decline from situational to individual interest across all groups (F (3, 52) = 9.23, p < 0.01), underscoring the difficulty of converting short-term curiosity into lasting commitment. These findings position classroom-management efficacy as a key moderator of engagement and support the use of adaptive simulations as sustainable tools for teacher education. By tailoring challenge levels and feedback to participants’ efficacy, guided simulations can foster deeper engagement and promote individualized growth—helping build resilient and well-prepared educators.

The growing adoption of XR-based simulations in educational settings has highlighted the need for intelligent systems to monitor, predict, and visualize learners' experiences in real-time. This paper presents the design and implementation of a Real-Time Intelligent Monitoring System (RTIMS) for XR Learning that predicts learners' Quality of Experience (QoE) by analyzing cognitive, affective, and behavior data collected during XR-based simulation learning. The system leverages 14 sets of multi-dimensional data from HoloLens 2, including head and hand positions and pupil size measurements captured via NEON eye-tracking glasses. Using the RTIMS, cognitive (e.g., attention, task load), affective (e.g., self-efficacy, learning satisfaction), and behavioral (e.g., task accuracy, completion time) domain data were gathered through controlled experiments and used to train deep learning models. The RTIMS predicted QoE scores and generated a Learning Effectiveness Index (LEI), visualized in a real-time dashboard, enabling continuous monitoring and predictive insights into learners' learning quality progress. This research lays the groundwork for advancing XR-based learning by enhancing learner monitoring through QoE prediction, improving instructional adaptability, and fostering high quality learning experiences.

This study aims to design and develop a collaborative simulation in extended reality (XR) environments that integrates a high-fidelity patient simulator to enhance emergency medical training, particularly in the context of Korea Advanced Life Support (KALS) education. In hospital settings, the ability to respond swiftly and effectively to cardiac arrest scenarios is essential, requiring structured team coordination, rapid decision-making, and effective communication. The team leader plays a central role in orchestrating the resuscitation process; however, existing training methods often fail to replicate the high-pressure, real-time decision-making requirements of actual emergency situations. To address these limitations, this study proposes an XR-based simulation that immerses learners in realistic emergency scenarios, allowing them to lead a virtual resuscitation team, issue commands, and implement life-saving interventions. By integrating a high-fidelity patient simulator with real-time interactive virtual team members, the training aims to enhance both technical and leadership skills. Additionally, the study will evaluate the effectiveness of XR-enhanced training in improving decision-making efficiency, team coordination, and preparedness for high-stakes medical emergencies. The broader implications of this research extend to scalability and adaptability, exploring how XR-based simulations can be expanded to other advanced life support training programs and integrated into global medical education frameworks.

This study proposes a design case for developing procedural information in extended reality (XR)-based Basic Life Support (BLS) training simulations, focusing on integrating immersive learning environments to align with XR-specific features. Procedural information was developed using the 4C/ID model, incorporating demonstration, just-in-time information, and corrective feedback were incorporated to support learners in effectively acquiring and automating BLS skills. Demonstrations provided visual and auditory guidance for observing task execution comprehensively, while just-in-time information offered step-specific procedural rules to facilitate cognitive integration during task performance. Corrective feedback enabled learners to identify and address errors, ensuring accurate procedural execution. The simulation emphasized spatial interaction and engagement to provide learners with authentic and immersive practice experiences that bridge theoretical knowledge and practical application. Expert reviews and iterative pilot testing refined the design, enhancing usability and instructional effectiveness. This study highlights the importance of procedural information design in creating XR-based simulations that promote learner engagement and skill acquisition. Future research should explore the effectiveness of multimodal interaction and specific XR design features in supporting cognitive and procedural learning outcomes, aiming to optimize knowledge retention and transfer across diverse educational contexts.

We propose design principles grounded in the theoretical frameworks of social presence and embodied interaction, to define the roles of telepresence robots and virtual agents as learning companions in educational content. Following a research-based design approach, we integrated these theories into design principles and developed a prototype of distance learning content that incorporates telepresence robots and virtual agents. The prototype was designed to enhance learner engagement and interaction in both physical and virtual learning environments. We demonstrate how the proposed design principles inform the design of learning tasks and the roles of learning companions. Additionally, we discuss the effectiveness of these learning companions in supporting learner interaction and reducing feelings of isolation, while considering the different affordances of telepresence robots and virtual agents. Finally, we propose areas for further research and potential improvements in the design and application of such learning technologies.