Media Effects Research Lab - Research Archive
Evaluating Interactivity Types for Data Visualization in Virtual Reality
Student Researcher(s)
Mark Simpson (Ph.D Candidate);
Jiayan Zhao (Ph.D Candidate);
Faculty Supervisor
This project was done based on COMM 517 course.
INTRODUCTION
3D virtual reality (VR) technology has long promised to provide new ways to view and interact with abstract data, but it has been held back by technological limitations a well as the difficulty of moving through 3D environments. Navigation, or viewpoint interaction, has been a continuing challenge for 3D visualization. Recent innovations in VR technology overcome previous limitations in terms of responsiveness, fidelity, and other aspect. However existing research has had mixed results on the most effective viewpoint interaction techniques.
RESEARCH QUESTION / HYPOTHESES
In a virtual environment, does being able to physically walk around a 3D scatterplot result in increased understanding of the scatterplot compared to standing still, and moving the scatterplot with an input device?
METHOD
A between-subjects (N = 20) experiment comparing two viewpoint interaction types conditions in a 3D graph understanding experiment. Specifically, users encountered a series of five scatterplots within a head-mounted display VR system and answered a series of questions about the scatterplots designed to measure their memory and understanding. In the first condition, participants were able to walk around a 3D scatterplot embedded in a VR environment, their virtual viewpoint being matched to their physical location and look direction by the system. In the second condition, users are immersed in the same VR environment, but do not move. Instead, they used a controller to rotate and move the scatterplot in order to view it from different sides.
RESULTS
No significant differences were found between conditions, likely due to the small sample size of our study, but exploratory ANOVAs revealed interactions between graph reading ability, mental rotation ability, and condition.
CONCLUSIONS/DISCUSSION
An exploratory analysis revealed that individual differences played a strong role depending on the condition. In particular, low spatial ability users were better supported by walking interaction rather than interaction using a controller. This has implications for the design of virtual reality data visualization systems, as technology for walking interaction are generally more costly.
For more details regarding the study contact
Dr. S. Shyam Sundar by e-mail at sss12@psu.edu or by telephone at (814) 865-2173