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Sankeye: A Visualization Technique for AOI Transitions

Published: 02 June 2020 Publication History

Abstract

Visually exploring AOI transitions aggregated from a group of eye tracked people is a challenging task. Many visualizations typically produce visual clutter or aggregate the temporal or visit order information in the data hiding the visual task solution strategies for the observer. In this paper we introduce the Sankeye technique that is based on the visual metaphor of Sankey diagrams applied to eye movement data, hence the name Sankeye. The technique encodes the frequencies of AOI transitions into differently thick rivers and subrivers. The distributions of the AOI transitions are visually represented by splitting and merging subrivers in a left-to-right reading direction. The technique allows to interactively adapt the number of predefined AOIs as well as the transition frequency number threshold with the goal to derive patterns and insights from eye movement data.

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Cited By

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  • (2023)Mobile Eye Tracking during Experimenting with Digital Scaffolding—Gaze Shifts between Augmented Reality and Experiment during Zinc Iodide Electrolysis Set-UpEducation Sciences10.3390/educsci1302017013:2(170)Online publication date: 6-Feb-2023
  • (2023)Optical Character Recognition (OCR)-Based and Gaussian Mixture Modeling-OCR-Based Slide-Level “With-Me-Ness”: Automated Measurement and Feedback of Learners’ Attention State during Video LecturesInternational Journal of Human–Computer Interaction10.1080/10447318.2023.220427240:15(3952-3971)Online publication date: 7-May-2023
  • (2022)Hierarchical Sankey Diagram: Design and EvaluationAdvances in Visual Computing10.1007/978-3-030-90436-4_31(386-397)Online publication date: 1-Jan-2022
  • Show More Cited By

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cover image ACM Conferences
ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications
June 2020
305 pages
ISBN:9781450371346
DOI:10.1145/3379156
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 June 2020

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Author Tags

  1. Areas of interest
  2. Eye tracking
  3. Information visualization
  4. Sankey diagrams
  5. Transition matrices

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Overall Acceptance Rate 69 of 137 submissions, 50%

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Cited By

View all
  • (2023)Mobile Eye Tracking during Experimenting with Digital Scaffolding—Gaze Shifts between Augmented Reality and Experiment during Zinc Iodide Electrolysis Set-UpEducation Sciences10.3390/educsci1302017013:2(170)Online publication date: 6-Feb-2023
  • (2023)Optical Character Recognition (OCR)-Based and Gaussian Mixture Modeling-OCR-Based Slide-Level “With-Me-Ness”: Automated Measurement and Feedback of Learners’ Attention State during Video LecturesInternational Journal of Human–Computer Interaction10.1080/10447318.2023.220427240:15(3952-3971)Online publication date: 7-May-2023
  • (2022)Hierarchical Sankey Diagram: Design and EvaluationAdvances in Visual Computing10.1007/978-3-030-90436-4_31(386-397)Online publication date: 1-Jan-2022
  • (2021)Aggregated Gaze Data Visualization Using Contiguous Irregular CartogramsDigital10.3390/digital10300101:3(130-144)Online publication date: 30-Jun-2021
  • (2021)Visual Analysis of Electronic Densities and Transitions in MoleculesComputer Graphics Forum10.1111/cgf.1430740:3(287-298)Online publication date: 29-Jun-2021
  • (2021)A Web-Based Eye Tracking Data Visualization ToolPattern Recognition. ICPR International Workshops and Challenges10.1007/978-3-030-68796-0_29(405-419)Online publication date: 21-Feb-2021

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