Particle selection and parameterisation in virtual reality - Keren, Gil Boaz (Department of Computer Science, 2025)
This dissertation investigates the application of Virtual Reality (VR) technology in enhancing the selection and parameterisation of astronomical data compared to traditional desktop environments. Through the development and evaluation of the Immersive Data Visualisation Interactive Explorer for Particle Rendering (iDaVIE-p), a novel software application designed for both VR and desktop interfaces, this study explores VR's potential to improve accuracy, efficiency, and user experience in scientific research, particularly in astronomy, where datasets are large and complex. The primary focus of this research is to establish whether VR technology surpasses desktop environments in terms of task performance metrics such as accuracy, efficiency, usability, workload, and flow and how parameter tuning influences these metrics. Our experimental design involves a hybrid of within and between-subject comparison, engaging participants in tasks that require selecting and adjusting parameters of celestial objects represented as particles. Participants utilised the iDaVIE-p software in both VR and desktop, providing feedback through established questionnaires like the System Usability Scale, NASA Task Load Index, and Flow State Scale. The results indicate that while accuracy remained comparable between VR and desktop interfaces, VR significantly enhanced efficiency, with tasks completed 28% faster on average. Additionally, VR outperformed desktop in usability, workload, and flow metrics, evidencing a more engaging and less taxing experience. Surprisingly, these benefits were realized even among participants with limited VR experience, underscoring VR's intuitive interaction with three-dimensional (3D) environments. However, the study found mixed outcomes regarding parameter tuning, showing minor improvements in accuracy but a similar minor decrease in efficiency, suggesting a possible tradeoff between the two and that further research is needed to optimise parameter adjustments for task performance. This dissertation underscores VR's transformative potential in scientific research, offering insights into its advantages over traditional desktop interfaces for interacting with complex, 3D datasets. The findings advocate for the broader adoption of VR technology in scientific settings where tasks and datasets are 3D, as was in our case, highlighting its capacity to enhance user satisfaction, efficiency, and engagement in data selection tasks.