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1. Visual Cues to Restore Student Attention based on Eye Gaze Drift, and Application to an Offshore Training System

   https://doi.org/10.1145/3357251.3360007  

   Yoshimura, Andrew; Khokhar, Adil; Borst, Christoph W (October 2019, ACM Symposium on Spatial User Interaction (SUI) 2019)

   Drifting student attention is a common problem in educational environments. We demonstrate 8 attention-restoring visual cues for display when eye tracking detects that student attention shifts away from critical objects. These cues include novel aspects and variations of standard cues that performed well in prior work on visual guidance. Our cues are integrated into an offshore training system on an oil rig. While students participate in training on the oil rig, we can compare our various cues in terms of performance and student preference, while also observing the impact of eye tracking. We demonstrate experiment software with which users can compare various cues and tune selected parameters for visual quality and effectiveness. 

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2. Robot-mediated Job Interview Training for Individuals with ASD: A Pilot Study

   https://doi.org/10.1109/RO-MAN57019.2023.10309611  

   Shahverdi, Pourya; Rousso, Katelyn; Bakhoda, Iman; Huang, Nathan; Rohrbeck, Kristin; Louie, Wing-Yue Geoffrey (August 2023, 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN))
3. Gaze transition entropy as a measure of attention allocation in a dynamic workspace involving automation

   https://doi.org/10.1038/s41598-024-74244-4  

   Cui, Zixin; Sato, Tetsuya; Jackson, Austin; Jayarathna, Sampath; Itoh, Makoto; Yamani, Yusuke (December 2024, Scientific Reports)

   Abstract Real-world work environments require operators to perform multiple tasks with continual support from an automated system. Eye movement is often used as a surrogate measure of operator attention, yet conventional summary measures such as percent dwell time do not capture dynamic transitions of attention in complex visual workspace. This study analyzed eye movement data collected in a controlled a MATB-II task environment using gaze transition entropy analysis. In the study, human subjects performed a compensatory tracking task, a system monitoring task, and a communication task concurrently. The results indicate that both gaze transition entropy and stationary gaze entropy, measures of randomness in eye movements, decrease when the compensatory tracking task required more continuous monitoring. The findings imply that gaze transition entropy reflects attention allocation of operators performing dynamic operational tasks consistently. 

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4. Gaze Entropy as a Measure of Player Performance and Its Correlation with Focus and Attention

   https://doi.org/10.1145/3715669.3725884  

   Pokkula, Saroj Kaashyap; Amresh, Ashish; Duval, Jared (May 2025, ACM)
5. Look Hear: Gaze Prediction for Speech-Directed Human Attention

   https://doi.org/10.1007/978-3-031-72946-1_14  

   Mondal, Sounak; Ahn, Seoyoung; Yang, Zhibo; Balasubramanian, Niranjan; Samaras, Dimitris; Zelinsky, Gregory; Hoai, Minh (October 2024, Lecture notes in computer science)