How Immersion and Self-Avatars in VR Affect Learning Programming and Computational Thinking in Middle School Education.

We present an empirical evaluation of immersion and self-avatars as compared to desktop viewing in Virtual Reality (VR) for learning computer programming and computational thinking in middle school education using an educational VR simulation. Students were asked to programmatically choreograph dance performances for virtual characters within an educational desktop application we built earlier called Virtual Environment Interactions (VEnvI). As part of a middle school science class, 90 students from the 6th and 7th grades participated in our study. All students first visually programmed dance choreography for a virtual character they created in VEnvI on a laptop. Then, they viewed and interacted with the resulting dance performance in a between-subjects design in one of the three conditions. We compared and contrasted the benefits of embodied immersive virtual reality (EVR) viewing utilizing a head-mounted display with a body-scaled and gender-matched self-avatar, immersive virtual reality only (IVR) viewing, and desktop VR (NVR) viewing with VEnvI on pedagogical outcomes, programming performance, presence, and attitudes towards STEM and computational thinking. Results from a cognition questionnaire showed that, in the learning dimensions of Knowledge and Understanding (Bloom's taxonomy) as well as Multistructural (SOLO taxonomy), participants in EVR and IVR scored significantly higher than NVR. Also, participants in EVR scored significantly higher than IVR. We also discovered similar results in objective programming performance and presence scores in VEnvI. Furthermore, students' attitudes towards computer science, programming confidence, and impressions significantly improved to be the highest in EVR and then IVR as compared to NVR condition. Our work suggests that educators and developers of educational VR simulations, who want to enhance knowledge and understanding as well as simultaneous acquisition of multiple abstract concepts, can do so by employing immersion and self-avatars in VR learning experiences.

The Security-Utility Trade-off for Iris Authentication and Eye Animation for Social Virtual Avatars.

The gaze behavior of virtual avatars is critical to social presence and perceived eye contact during social interactions in Virtual Reality. Virtual Reality headsets are being designed with integrated eye tracking to enable compelling virtual social interactions. This paper shows that the near infra-red cameras used in eye tracking capture eye images that contain iris patterns of the user. Because iris patterns are a gold standard biometric, the current technology places the user's biometric identity at risk. Our first contribution is an optical defocus based hardware solution to remove the iris biometric from the stream of eye tracking images. We characterize the performance of this solution with different internal parameters. Our second contribution is a psychophysical experiment with a same-different task that investigates the sensitivity of users to a virtual avatar's eye movements when this solution is applied. By deriving detection threshold values, our findings provide a range of defocus parameters where the change in eye movements would go unnoticed in a conversational setting. Our third contribution is a perceptual study to determine the impact of defocus parameters on the perceived eye contact, attentiveness, naturalness, and truthfulness of the avatar. Thus, if a user wishes to protect their iris biometric, our approach provides a solution that balances biometric protection while preventing their conversation partner from perceiving a difference in the user's virtual avatar. This work is the first to develop secure eye tracking configurations for VR/AR/XR applications and motivates future work in the area.