Gaze for Dyads & Blinks for Triads: Task Engagement Predictors in Free-Flow VR Interactions

Carlos González Díaz, Georgiana Cristina Dobre, Elisabeth André, Cristina Conati

Abstract: This paper investigates in an exploratory manner small group interactions in Social VR classroom conversations using eye-tracking data (group eye-gaze configurations, blink rate and blink duration) in terms of task engagement. Task engagement has been previously studied in educational settings as it is an important construct for learning. Prior research showed that eye signals in groups can assess rapport, attention, agreement or communication engagement. However, little research focused on whether eye-tracking data can predict task engagement in educational small group Social VR conversations. We conducted an exploratory study with 52 pedagogy university students (8 dyads, 12 triads) engaging in free-flow conversations on a given topic. Results indicated that eye-tracking data could predict task engagement using multiple linear regression analysis. However, group eye-gaze configurations were the significant predictors in dyads, whereas the blink rate was the strongest predictor associated with task engagement in triads. Based on these findings, we discuss the higher complexity in triadic gaze dynamics and why the blink rate was the task engagement predictor. On the other hand, the simplicity of dyadic gaze configurations might as well explain why it was found as a predictor. Furthermore, we argue that future work can explore machine learning detectors of task engagement dependent of group size. The results from this exploratory study help understanding non-verbal group interactions in VR and contribute to further work on engagement models for more socially-abled virtual experiences in Social VR applications.

External IDs:dblp:conf/aivr/DiazDAC26