# Behavioral Engagement in VR-Based Sign Language Learning: Visual Attention as a Predictor of Performance and Temporal Dynamics

arXiv: https://arxiv.org/abs/2603.19535 · HTML: https://mlsanity.com/paper/2603.19535/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [Inclusive AR/VR: Accessibility Barriers for Immersive Technologies](https://mlsanity.com/paper/2304.13465/) (2023)
- [Training program on sign language: social inclusion through Virtual Reality in ISENSE project](https://mlsanity.com/paper/2401.08714/) (2024)
- [Enhancing Sign Language Teaching: A Mixed Reality Approach for Immersive Learning and Multi-Dimensional Feedback](https://mlsanity.com/paper/2404.10490/) (2024)
- [The Impact of 2D and 3D Gamified VR on Learning American Sign Language](https://mlsanity.com/paper/2405.08908/) (2024)
- [Virtual Reality in Sign Language Education: Opportunities, Challenges, and the Road Ahead](https://mlsanity.com/paper/2508.17362/) (2025)

## Abstract (arXiv)

This study analyzes behavioral engagement in SONAR, a virtual reality application designed for sign language training and validation. We focus on three automatically derived engagement indicators (Visual Attention (VA), Video Replay Frequency (VRF), and Post-Playback Viewing Time (PPVT)) and examine their relationship with learning performance. Participants completed a self-paced Training phase, followed by a Validation quiz assessing retention. We employed Pearson correlation analysis to examine the relationships between engagement indicators and quiz performance, followed by binomial Generalized Linear Model (GLM) regression to assess their joint predictive contributions. Additionally, we conducted temporal analysis by aggregating moment-to-moment VA traces across all learners to characterize engagement dynamics during the learning session. Results show that VA exhibits a strong positive correlation with quiz performance,followed by PPVT, whereas VRF shows no meaningful association. A binomial GLM confirms that VA and PPVT are significant predictors of learning success, jointly explaining a substantial proportion of performance variance. Going beyond outcome-oriented analysis, we characterize temporal engagement patterns by aggregating moment-to-moment VA traces across all learners. The temporal profile reveals distinct attention peaks aligned with informationally dense segments of both training and validation videos, as well as phase-specific engagement dynamics, including initial acclimatization, oscillatory attention cycles during learning, and pronounced attentional peaks during assessment. Together, these findings highlight the central role of sustained and strategically allocated visual attention in VR-based sign language learning and demonstrate the value of behavioral trace data for understanding and predicting learner engagement in immersive environments.