Optimizing Performance and Satisfaction in Matching and Movement Tasks in Virtual Reality with Interventions Using the Data Visualization Literacy Framework
release_utwawzev6zdxlpft4baarmsb54
by
Andreas Bueckle, Kilian Buehling, Patrick C. Shih, Katy Borner
2021
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 (p(rotation) = 0.031, p(satisfaction) = 0.040).
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