Date of Award:

8-2025

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Computer Science

Committee Chair(s)

Isaac Cho

Committee

Isaac Cho

Committee

John Edwards

Committee

Soukaina Filali Boubrahimi

Committee

Steve Petruzza

Committee

Yalong Yang

Abstract

This research investigates how virtual reality (VR) can be utilized effectively for users to explore data. Traditional data analysis typically takes place on flat, two-dimensional computer screens. In contrast, immersive technologies like VR offer a three-dimensional environment where users can engage with data more naturally and intuitively. Such immersive experiences have the potential to improve comprehension, memory, and insight during data analysis tasks. Despite this potential, several challenges remain in making immersive analytics practical and effective. Key questions include which types of 3D interaction techniques are most helpful and how accurately people perceive visual information in immersive environments. To address these issues, this research presents three interconnected studies. The first examines how 3D interaction methods can support spatial working memory when analyzing data in VR. The second investigates how well users perceive visual variables on virtual wall-sized displays and whether interaction techniques can improve this perception. The third explores how the arrangement of virtual displays in immersive workspaces influences users’ understanding of visual information. Together, these studies aim to advance our understanding of the benefits of immersive analytics compared to traditional 2D approaches, and to identify effective 3D interaction strategies that support data analysis in virtual environments.

Checksum

3fe8094bd30d705ec0c11b3af4e43209

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

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