Arun K. Kulshreshth

1. Virtual reality (VR) has emerged as a promising platform for immersive education, offering opportunities to enhance engagement, presence, and experiential learning. At the same time, immersive s...

Virtual reality offers immersive educational environments but presents challenges in accurately detecting user distraction. Prior studies often define distraction based solely on the presence of external interventions, overlooking spontaneous cognitive lapses and assuming that all interventions are effective distractors. In this study, we investigate the feasibility of distraction detection in VR lectures using machine learning models trained on physiological sensor data, including electroencephalography, eye tracking, and heart rate. We evaluate a binary classification model intended for future real-time deployment, incorporating interventioninduced and self-reported cognitive distractions. Our best performing binary model achieved an F1 score of 87.3%. We also explore a multiclass classification approach across five distraction types, which yielded a lower F1 score of 32.8%. Our preprocessing pipeline and model architecture are optimized with real-time compatibility in mind, supporting future applications in adaptive educational VR systems. This work contributes to developing distractionaware VR learning environments by demonstrating the potential for scalable, real-time distraction classification using multimodal physiological data.

Achieving precise real-time localization and ensuring robustness are critical challenges in multi-user mobile AR applications. Leveraging collaborative information to augment tracking accuracy on lightweight devices and fortify overall system robustness emerges as a crucial necessity. In this paper, we propose a robust centralized collaborative rnulti-agent VI-SLAM system for mobile AR interaction and server-side efficient consistent mapping. The system deploys a lightweight VIO frontend on mobile devices for real-time tracking, and a backend running on a remote server to update multiple submaps. When overlapping areas between submaps across agents are detected, the system performs submap fusion to establish a globally consistent map. Additionally, we propose a map registration and fusion strategy based on covisibility areas for online registration and fusion in multi-agent scenarios. To improve the tracking accuracy of the frontend on agent, we introduce a strategy for updating the global map to the local map at a moderate frequency between the camera-rate pose estimation of the frontend VIO and the low-frequency global map optimization, using a tightly coupled strategy to achieve consistency of the multi-agent frontend poses estimation in the global map. The effectiveness of the proposed method is further confirmed by executing backend mapping on the server and deploying VIO frontends on multiple mobile devices for AR demostration. Additionally, we discuss the scalability of the proposed system by analyzing network traffic, synchronization frequency, and other factors at both the agent and server ends.

Maintaining player engagement is pivotal for video game success, yet achieving the optimal difficulty level that adapts to diverse player skills remains a significant challenge. Initial difficulty settings in games often fail to accommodate the evolving abilities of players, necessitating adaptive difficulty mechanisms to keep the gaming experience engaging. This study introduces a custom first-person-shooter (FPS) game to explore Dynamic Difficulty Adjustment (DDA) techniques, leveraging both performance metrics and emotional responses gathered from physiological sensors. Through a within-subjects experiment involving casual and experienced gamers, we scrutinized the effects of various DDA methods on player performance and self-reported game perceptions. Contrary to expectations, our research did not identify a singular, most effective DDA strategy. Instead, findings suggest a complex landscape where no one approach—be it performance-based, emotion-based, or a hybrid—demonstrably surpasses static difficulty settings in enhancing player engagement or game experience. Noteworthy is the data’s alignment with Flow Theory, suggesting potential for the Emotion DDA technique to foster engagement by matching challenges to player skill levels. However, the overall modest impact of DDA on performance metrics and emotional responses highlights the intricate challenge of designing adaptive difficulty that resonates with both the mechanical and emotional facets of gameplay. Our investigation contributes to the broader dialogue on adaptive game design, emphasizing the need for further research to refine DDA approaches. By advancing our understanding and methodologies, especially in emotion recognition, we aim to develop more sophisticated DDA strategies. These strategies aspire to dynamically align game challenges with individual player states, making games more accessible, engaging, and enjoyable for a wider audience.

This study investigated undergraduate nursing students' perceptions of integrating virtual reality (VR) technology into a community nursing course, specifically home and environmental patient safety assessments. This study used a mixed-methods approach with an explanatory design. Participants were recruited from a community health course. The students engaged in a VR simulation involving home visits using the vizHome platform. Quantitative and qualitative data were gathered using the Technology Acceptance Model (TAM) and the System Usability Scale (SUS). Semi-structured interviews were conducted. Quantitative data were collected through Qualtrics and secure Zoom connections for the interviews. The data were analyzed with SPSS and MAXQDA. The results indicated that participants perceived VR as valuable and easy to use for learning home assessment skills. The System Usability Scale (SUS) score revealed room for improvement. Technical limitations were identified as challenges that must be addressed to enhance the user experience. Participants acknowledged VR's potential to supplement traditional learning methods, providing safe and realistic exposure to diverse home environments. While VR was seen as beneficial, it was not considered a replacement for actual home visits in community nursing education. This study provided clinical teaching evidence on the usability and student perceptions of VR in community courses.

We assess the effectiveness of attention guidance cues in an educational platform in local high schools with real students. Three eye-tracked visual cues, previously assessed for their ability to guide and restore attention, are compared against a baseline absence of cue in a VR field trip of a virtual solar energy field. Students experienced four presentations on solar energy production including in-world animations and teacher imagery, in three of which the visual cues guided attention to the relevant object or teacher in the scene. Attention guidance using visual cues is commonly studied using “search and selection” style tasks, but has not been studied in the context of maintaining attention in real-world environments.

Virtual reality (VR) makes learning more interesting for students and could help them remember what they have learned better than traditional methods. However, a student could get distracted in a VR environment because of stress, wandering thoughts, unwanted noise, outside sounds, etc. Distractions could be classified as either external (due to the environment) or internal (due to internal thoughts). To identify external distractions, previous researchers have used eye-gaze data. Eye-gaze data cannot, however, detect internal distractions because a user may be looking at the educational material in VR while also thinking about something else. We explored the usage of electroencephalogram (EEG) data to detect internal distractions. We designed an educational VR environment and trained three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and k-nearest-neighbors (kNN), to detect internal distractions of students. For data labeling, we considered two window lengths (20 and 30 seconds) starting at 5 seconds after the distraction task started. We did cross-subject and cross-session tests, and our results show that kNN provides a better accuracy (64%) compared to RF and SVM. We also found that the shorter window length of 20 seconds provided a slightly better accuracy then the 30 second window. Our results are not far from such random guessing. Therefore, our contribution lies more in the fostering of ideas for future work that must employ more advanced and sophisticated techniques.

Virtual Reality (VR) provides a more engaging learning experience to students and could improve knowledge retention compared to traditional learning methods. However, a student could get distracted in the VR environment due to stress, mind-wandering, unwanted noise, external sounds, etc. Distractions could be classified as either external (due to environment) or internal (due to internal thoughts). Past researchers have used eye-gaze data to detect external distractions. However, eye-gaze data can not measure internal distractions since a user could look at the educational content and may be thinking about something else. We explored the usage of electroencephalogram (EEG) data to detect internal distraction. We designed an educational VR environment and trained three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA), to detect internal distractions of students. Our preliminary study results show that RF provides a better accuracy (98%) compared to SVM and LDA.