Evidence map›Paper›PMID 42315946›Full record

ArticleScientific reports2026

A visualization tool for eye tracking time series analysis supporting teachers and psychologists.

Abdul Rehman, Ilona Heldal, Jerry Chun-Wei Lin

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Abdul RehmanDepartment of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. arj@hvl.no.
Ilona HeldalDepartment of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway.
Jerry Chun-Wei LinDepartment of Distributed Systems and IT Devices, Silesian University of Technology, Gliwice, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Eye Tracking (ET) can help improve understanding of visual attention in computer-supported interactive environments. In attention tasks, distinguishing between relevant target objects and distractors is crucial for effective performance, yet the underlying gaze patterns that drive successful task completion remain incompletely understood. Traditional gaze analyses provide limited insight into the temporal dynamics of attention allocation and the relationship between gaze behavior and task performance. When applied to complex visual search scenarios, current gaze analysis methods face several limitations, including the isolation of measurements in dynamic environments, visual stability, search efficiency, and the task-solving processes involved. This paper proposes an analysis tool, VisiTrail, that considers time-series eye-tracking data on task performance and gaze measures; temporal pattern analysis that reveals how attention evolves throughout task performance; object-click sequence tracking that directly links visual attention to user actions; and performance metrics that quantify both accuracy and efficiency of right actions. The proposed analysis is applied to data collected from the Mushroom Hunter serious game, a multilevel visual search task in which participants identify target mushrooms among distractors of increasing complexity across three difficulty levels. This tool focuses on two scenarios: subject-specific analysis and general analysis across all subjects from which a project collected data from Romania and Portugal. Subject-specific analysis uses only one participant's data and yields two types of results: a first-level overall analysis that provides detailed insights and a multilevel analysis that compares performance across all three levels, where Level 1 presents the easiest task with few distractors, Level 2 increases difficulty by adding more similar distractors, and Level 3 is the hardest with many closely resembling distractors and more complex layouts. The generalized analysis reveals that gaze stability (Fixation %) was broadly consistent across both cohorts. Romanian participants exhibited faster median reaction times than Portuguese participants; however, given the small sample size (N=7 per cohort), the presence of data-quality anomalies in three Portuguese sessions, and the absence of inferential statistical testing, this difference should be regarded as preliminary and hypothesis-generating rather than conclusive. By using standardized parameters, the results reduce accidental interactions and hardware noise, providing a reliable methodological basis for preliminary analysis and highlighting the need for larger sample sizes to establish statistical validity.

Indexed as

AttentionEye MovementsEye-Tracking TechnologyFixation, OcularHumansPsychologyTask Performance and AnalysisBehavior analysisEducational technologyEye-tracking technologyLearning analyticsTime series analysisVisual attentionVisual cognition

Identifiers

PMID42315946
PMCPMC13547336

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.