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A visualization tool for eye tracking time series analysis supporting teachers and psychologists
Abdul Rehman1, Ilona Heldal2, Jerry Chun-Wei Lin3
1Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. arj@hvl.no.
Scientific Reports
|June 18, 2026
Summary
This study introduces VisiTrail, a novel tool for analyzing eye-tracking (ET) data to understand visual attention dynamics in interactive environments. VisiTrail links gaze patterns to task performance, offering deeper insights than traditional methods.
Area of Science:
- Human-Computer Interaction
- Cognitive Science
- Usability Engineering
Background:
- Understanding visual attention is key for optimizing interactive systems.
- Traditional eye-tracking analysis methods lack temporal dynamics and task performance links.
- Complex visual search tasks present challenges for current gaze analysis techniques.
Purpose of the Study:
- To introduce VisiTrail, an advanced analysis tool for time-series eye-tracking data.
- To reveal temporal attention patterns and their relationship to user actions and task performance.
- To provide a methodological basis for analyzing visual attention in complex interactive environments.
Main Methods:
- VisiTrail integrates time-series eye-tracking data with task performance metrics.
- Temporal pattern analysis tracks attention evolution during task completion.
- Object-click sequence tracking links visual attention to user actions.
- The tool was applied to data from the Mushroom Hunter serious game across three difficulty levels.
Main Results:
- VisiTrail enables subject-specific and generalizable analyses of visual attention.
- Gaze stability (Fixation %) was consistent across Romanian and Portuguese participants.
- Preliminary findings suggest faster reaction times in Romanian participants, requiring further investigation with larger sample sizes.
Conclusions:
- VisiTrail offers a robust method for analyzing complex eye-tracking data.
- The tool enhances understanding of visual attention allocation and its impact on performance.
- Standardized analysis reduces noise and provides a reliable basis for future research, emphasizing the need for larger sample sizes.
