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1. Exploring Eye Gaze Visualization Techniques for Identifying Distracted Students in Educational VR

   https://doi.org/10.1109/VR46266.2020.00009  

   Rahman, Yitoshee; Asish, Sarker M.; Fisher, Nicholas P.; Bruce, Ethan C.; Kulshreshth, Arun K.; Borst, Christoph W. (March 2020, 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR))

   Virtual Reality (VR) headsets with embedded eye trackers are appearing as consumer devices (e.g. HTC Vive Eye, FOVE). These devices could be used in VR-based education (e.g., a virtual lab, a virtual field trip) in which a live teacher guides a group of students. The eye tracking could enable better insights into students’ activities and behavior patterns. For real-time insight, a teacher’s VR environment can display student eye gaze. These visualizations would help identify students who are confused/distracted, and the teacher could better guide them to focus on important objects. We present six gaze visualization techniques for a VR-embedded teacher’s view, and we present a user study to compare these techniques. The results suggest that a short particle trail representing eye trajectory is promising. In contrast, 3D heatmaps (an adaptation of traditional 2D heatmaps) for visualizing gaze over a short time span are problematic. 

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2. Deep Learning on Eye Gaze Data to Classify Student Distraction Level in an Educational VR Environment

   https://doi.org/10.2312/egve.20211326  

   Asish, S. M.; Hossain, E.; Kulshreshth. A. K.; Borst, C. W. (January 2021, International Conference on Artificial Reality and Telexistence Eurographics Symposium on Virtual Environments (ICAT-EGVE))

   Educational VR may increase engagement and retention compared to traditional learning, for some topics or students. However, a student could still get distracted and disengaged due to stress, mind-wandering, unwanted noise, external alerts, etc. Student eye gaze can be useful for detecting distraction. For example, we previously considered gaze visualizations to help teachers understand student attention to better identify or guide distracted students. However, it is not practical for a teacher to monitor a large numbers of student indicators while teaching. To help filter students based on distraction level, we consider a deep learning approach to detect distraction from gaze data. The key aspects are: (1) we created a labeled eye gaze dataset (3.4M data points) from an educational VR environment, (2) we propose an automatic system to gauge a student's distraction level from gaze data, and (3) we apply and compare three deep neural classifiers for this purpose. A proposed CNN-LSTM classifier achieved an accuracy of 89.8\% for classifying distraction, per educational activity section, into one of three levels. 

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3. Redirecting Desktop Interface Input to Animate Cross-Reality Avatars

   https://doi.org/10.1109/VR51125.2022.00106  

   Woodworth, Jason W.; Broussard, David; Borst, Christoph W. (March 2022, 2022 IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR))

   We present and evaluate methods to redirect desktop inputs such as eye gaze and mouse pointing to a VR-embedded avatar. We use these methods to build a novel interface that allows a desktop user to give presentations in remote VR meetings such as conferences or classrooms. Recent work on such VR meetings suggests a substantial number of users continue to use desktop interfaces due to ergonomic or technical factors. Our approach enables desk-top and immersed users to better share virtual worlds, by allowing desktop-based users to have more engaging or present "cross-reality" avatars. The described redirection methods consider mouse pointing and drawing for a presentation, eye-tracked gaze towards audience members, hand tracking for gesturing, and associated avatar motions such as head and torso movement. A study compared different levels of desktop avatar control and headset-based control. Study results suggest that users consider the enhanced desktop avatar to be human-like and lively and draw more attention than a conventionally animated desktop avatar, implying that our interface and methods could be useful for future cross-reality remote learning tools. 

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4. A Low-Cost Wireless System Implementation for Interactive and Immersive Teaching

   https://doi.org/10.1145/3209582.3225208  

   Weng, Minghui; Kong, Xiangqi; Huang, Lianfen; Li, Bin (June 2018, Mobihoc '18 Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing)

   In recent years, virtual/augmented reality (VR/AR) technology has received great attention due to its capability of creating various levels of immersive experiences. However, current wireless VR/AR devices are quite expensive, which hinders its large-scale deployment in practice. In this demo, we present a wireless interactive VR/AR teaching system based on popular Android phones. In such a demo, when a teacher explains a 3D model, multiple students can see it from exactly the same perspective as the teacher does through VR/AR glasses. When one student has a concern or question regarding a particular part of the 3D model, he/she can point it out, and a corresponding blue cursor will appear on screens of all users. Moreover, in the absence of 3D models in Android phones, we broadcast 3D models based on their visual priorities. 

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5. Synchronized eye movements predict test scores in online video education

   https://doi.org/10.1073/pnas.2016980118  

   Madsen, Jens; Júlio, Sara U.; Gucik, Pawel J.; Steinberg, Richard; Parra, Lucas C. (January 2021, Proceedings of the National Academy of Sciences)

   null (Ed.)

   Experienced teachers pay close attention to their students, adjusting their teaching when students seem lost. This dynamic interaction is missing in online education. We hypothesized that attentive students follow videos similarly with their eyes. Thus, attention to instructional videos could be assessed remotely by tracking eye movements. Here we show that intersubject correlation of eye movements during video presentation is substantially higher for attentive students and that synchronized eye movements are predictive of individual test scores on the material presented in the video. These findings replicate for videos in a variety of production styles, for incidental and intentional learning and for recall and comprehension questions alike. We reproduce the result using standard web cameras to capture eye movements in a classroom setting and with over 1,000 participants at home without the need to transmit user data. Our results suggest that online education could be made adaptive to a student’s level of attention in real time. 

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