Identifying fixations and saccades in eye-tracking protocols
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Trials and tribulations of using an eye-tracking system
CHI '00 Extended Abstracts on Human Factors in Computing Systems
The incomplete fixation measure
Proceedings of the 2008 symposium on Eye tracking research & applications
Shifts in reported gaze position due to changes in pupil size: ground truth and compensation
Proceedings of the Symposium on Eye Tracking Research and Applications
Gaze map matching: mapping eye tracking data to geographic vector features
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Tracking learners' visual attention during a multimedia presentation in a real classroom
Computers & Education
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Data quality is essential to the validity of research results and to the quality of gaze interaction. We argue that the lack of standard measures for eye data quality makes several aspects of manufacturing and using eye trackers, as well as researching eye movements and vision, more difficult than necessary. Uncertainty regarding the comparability of research results is a considerable impediment to progress in the field. In this paper, we illustrate why data quality matters and review previous work on how eye data quality has been measured and reported. The goal is to achieve a common understanding of what data quality is and how it can be defined, measured, evaluated, and reported.