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Virtual reality (VR) has seen increased use for training and instruction.Designers can enable VR users to gain insights into their own performance byvisualizing telemetry data from their actions in VR. Our ability to detectpatterns and trends visually suggests the use of data visualization as a toolfor users to identify strategies for improved performance. Typical tasks in VRtraining scenarios are manipulation of 3D objects (e.g., for learning how tomaintain a jet engine) and navigation (e.g., to learn the geography of abuilding or landscape before traveling on-site). In this paper, we present theresults of the RUI VR (84 subjects) and Luddy VR studies (68 subjects), whereparticipants were divided into experiment and control cohorts. All subjectsperformed a series of tasks: 44 cube-matching tasks in RUI VR and 48 navigationtasks through a virtual building in Luddy VR (all divided into two sets). AllLuddy VR subjects used VR gear; RUI VR subjects were divided across threesetups: 2D Desktop (with laptop and mouse), VR Tabletop (in VR, sitting at atable), and VR Standup (in VR, standing). In an intervention called "Reflectivephase," the experiment cohorts were presented with data visualizations,designed with the Data Visualization Literacy Framework (DVL-FW), of the datathey generated during the first set of tasks before continuing to the secondpart of the study. For Luddy VR, we found that experiment users hadsignificantly faster completion times in their second trial (p = 0.014) whilescoring higher in a mid-questionnaire about the virtual building (p = 0.009).For RUI VR, we found no significant differences for completion time andaccuracy between the two cohorts in the VR setups; however, 2D Desktop subjectsin the experiment cohort had significantly higher rotation accuracy as well assatisfaction (p(rotation) = 0.031, p(satisfaction) = 0.040).

frontiers in virtual reality

Optimizing Performance and Satisfaction in Matching and Movement Tasks  in Virtual Reality with Interventions Using the Data Visualization Literacy  Framework