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User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities
Abstract
Information Visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to each individual user. To this end, this paper presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict the user's visualization tasks, as well as user cognitive abilities including perceptual speed, visual working memory, and verbal working memory. We show that such predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are discussed in view of designing visualization systems that can adapt to each individual user in real-time.
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During handwriting with a stylus, velocity, frequency, trajectory duration, and pressure of strokes were studied as indicators for cognitive workload.
“During handwriting with a stylus, the velocity [211,260], frequency [210], trajectory duration [209], and pressure of strokes [260] were studied as indicators for cognitive workload.”
From A Survey on Measuring Cognitive Workload in Human-Computer Interaction (2023) · cites this paper as [210]
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A Survey on Measuring Cognitive Workload in Human-Computer Interaction
Kosch, T., Karolus, J., Zagermann, J., Reiterer, H., Schmidt, A., & Woźniak, P. W. (2023) · ACM Computing Surveys · doi:10.1145/3582272 · Cited by 304
“…165, 184, 193, 228, 236, 258] Eye Blinks [4, 5, 27, 43–47, 76, 87, 99, 175, 182, 184, 236, 268] Fixations [1, 10, 54, 68, 99, 140, 153, 184, 219, 228] Smooth Pursuits and Ocular Movements [133] Cardiovascular Heart Rate [4, 10, 25, 27, 40, 41, 49, 50, 76, 78, 82, 85, 87, 99, 106, 146, 152, 160,…”
Metadata
- Authors
- Steichen, B., Carenini, G., & Conati, C.
- Affiliations
- University of British Columbia (CA)
- Venue
- Proceedings of the 2013 international conference on Intelligent user interfaces
- Publisher
- ACM
- Published
- 2013-03-19
- Pages
- 317-328
- OpenAlex
- W1967451046
- Cited by
- 146
- References
- 1
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 10 · 2023: 5 · 2022: 11 · 2021: 13 · 2020: 13 · 2019: 15 · 2018: 6 · 2017: 11 · 2016: 9 · 2015: 16 · 2014: 18 · 2013: 10
- FWCI
- 11.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- information visualization, gaze patterns, cognitive ability, perceptual speed, visual working memory, verbal working memory, real-time adaptation, user preferences
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