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A-DisETrac Advanced Analytic Dashboard for Distributed Eye Tracking
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Author(s): Yasasi Abeysinghe (Old Dominion University, USA), Bhanuka Mahanama (Old Dominion University, USA), Gavindya Jayawardena (Old Dominion University, USA), Yasith Jayawardana (Old Dominion University, USA), Mohan Sunkara (Old Dominion University, USA), Andrew T. Duchowski (Clemson University, USA), Vikas Ashok (Old Dominion University, USA)and Sampath Jayarathna (Old Dominion University, USA)
Copyright: 2024
Volume: 15
Issue: 1
Pages: 20
Source title:
International Journal of Multimedia Data Engineering and Management (IJMDEM)
Editor(s)-in-Chief: Chengcui Zhang (University of Alabama at Birmingham, USA)and Shu-Ching Chen (University of Missouri-Kansas City, United States)
DOI: 10.4018/IJMDEM.341792
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Abstract
Understanding how individuals focus and perform visual searches during collaborative tasks can help improve user engagement. Eye tracking measures provide informative cues for such understanding. This article presents A-DisETrac, an advanced analytic dashboard for distributed eye tracking. It uses off-the-shelf eye trackers to monitor multiple users in parallel, compute both traditional and advanced gaze measures in real-time, and display them on an interactive dashboard. Using two pilot studies, the system was evaluated in terms of user experience and utility, and compared with existing work. Moreover, the system was used to study how advanced gaze measures such as ambient-focal coefficient K and real-time index of pupillary activity relate to collaborative behavior. It was observed that the time a group takes to complete a puzzle is related to the ambient visual scanning behavior quantified and groups that spent more time had more scanning behavior. User experience questionnaire results suggest that their dashboard provides a comparatively good user experience.
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