Abstract
Virtual reality (VR) has seen increased use for training and instruction. Designers can enable VR users to gain insights into their own performance by visualizing telemetry data from their actions in VR. Our ability to detect patterns and trends visually suggests the use of data visualization as a tool for users to identify strategies for improved performance. Typical tasks in VR training scenarios are manipulation of 3D objects (e.g., for learning how to maintain a jet engine) and navigation (e.g., to learn the geography of a building or landscape before traveling on-site). In this paper, we present the results of the RUI VR (84 subjects) and Luddy VR studies (68 subjects), where participants were divided into experiment and control cohorts. All subjects performed a series of tasks: 44 cube-matching tasks in RUI VR, and 48 navigation tasks through a virtual building in Luddy VR (all divided into two sets). All Luddy VR subjects used VR gear. RUI VR subjects were divided across three setups: 2D Desktop (with laptop and mouse), VR Tabletop (in VR, sitting at a table), and VR Standup (in VR, standing). In an intervention called “Reflective phase,” the experiment cohorts were presented with data visualizations, designed with the Data Visualization Literacy Framework (DVL-FW), of the data they generated during the first set of tasks before continuing to the second part of the study. For Luddy VR, we found that experiment users had significantly faster completion times in their second trial (p= 0.014) while scoring higher in a mid-questionnaire about the virtual building (p= 0.009). For RUI VR, we found no significant differences for completion time and accuracy between the two cohorts in the VR setups. however, 2D Desktop subjects in the experiment cohort had significantly higher rotation accuracy as well as satisfaction (protation= 0.031,psatisfaction= 0.040). We conclude with suggestions for adjustments to the Reflective phase to boost user performance before generalizing our findings to performance improvement in VR with data visualizations.
Highlights
Due to decreasing cost and an increasing amount of hardware choice, Virtual reality (VR) has become a popular entertainment tool
Of special interest is the implementation of four interaction types: We enabled the subjects to filter their data by time stamp or graphic symbol (RUI VR) and task number; users could navigate freely around their data, which was displayed in its original spatial context on a 1:1 scale (RUI VR) and minimized (Luddy VR); it was possible to play back the data by time stamp in different speeds via a time slider (RUI VR); and subjects could select bars in a bar graph and apply filters correspondingly to view only specific tasks based on their completion time
For the Registration User Interface (RUI) VR study, we found no significant differences between the two VR cohorts for mean position accuracy, rotation accuracy, and completion time
Summary
Due to decreasing cost and an increasing amount of hardware choice, VR has become a popular entertainment tool. Depending on the hardware and the needs of the application, users of VR equipment generate position and rotation data at a rate of up to 120 Hz, and every button press can be logged and associated with a time stamp via telemetry. In addition to these physical variables, additional data can be derived via computation at runtime or in later analysis, allowing designers and researchers to measure a user’s performance and behavior when completing tasks such as arranging objects or navigating spaces. The visual primacy of VR, along with the availability of user data, suggests that data visualization is a good tool to allow users to gain insights into their own performance
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