In networked virtual reality (VR), user behaviors, individual differences, and group dynamics can serve as important signals for future speech behaviors, such as who the next speaker will be and the timing of turn-taking behaviors. The ability to predict and understand these behaviors offers opportunities to provide adaptive and personalized assistance, for example helping users with varying sensory abilities navigate complex social scenes and instantiating virtual moderators with natural behaviors. In this work, we predict turn-taking behaviors using features extracted based on social dynamics literature. We discuss results from a large-scale VR classroom dataset consisting of 77 sessions and 1660 minutes of small-group social interactions collected over four weeks. In our evaluation, gradient boosting classifiers achieved the best performance, with accuracies of 0.71-0.78 AUC (area under the ROC curve) across three tasks concerning the ''what'', ''who'', and ''when'' of turn-taking behaviors. In interpreting these models, we found that group size, listener personality, speech-related behavior (e.g., time elapsed since the listener's last speech event), group visual attention (e.g., the group's head orientation towards the speaker), and the listener and previous speaker's head pitch, head y-axis position, and left hand y-axis position more saliently influenced predictions. Results suggested that these features remain reliable indicators in novel social VR settings, as prediction performance is robust over time and with groups and activities not used in the training dataset. We discuss theoretical and practical implications of the work.

Social Virtual Reality (VR) typically entails users interacting in real time. However, asynchronous Social VR presents the possibility of combining the convenience of asynchronous communication with the high presence of VR. Because the tools to easily record and replay VR social interactions are fairly new, scholars have not yet examined how users perceive asynchronous VR social interactions, and how nonverbal transformations of recorded interactions influence user behavior. In this work, we study nonverbal transformations of group interactions around proxemics and gaze and present results from an exploratory user study (N=128) investigating their effects. We found that the combination of spatial accommodation and added gaze increases social presence, perceived attention, and mutual gaze. Results also showed an inverse relationship between interpersonal distance and perceived levels of dominance and threat of the recorded group. Finally, we outline implications for educators and virtual meeting organizers to incorporate these transformations into real-world scenarios.

Current research on immersive technologies has widely described their application in K-12 settings, with evidence of their goals, approaches, and potential to enhance learning. However, there is a dearth of studies describing those technologies being used as authoring tools by students. This paper investigates how K-12 students apply virtual and augmented reality technologies as tools for designing experiments and solutions for their science and engineering projects. Study 1 analyzed 12 projects presented at a FEBRACE national science fair between 2015 and 2020 and Study 2 investigated, via an online questionnaire, the motivations and challenges from students who worked on those projects. Results include the context in which the projects were developed, motivation for using virtual and augmented reality technologies, fields in which they were applied, and types of software and hardware used. Findings inform how future research can build on those experiences to integrate immersive technologies in K-12 education.