The determinants of web page viewing behavior: an eye-tracking study
Proceedings of the 2004 symposium on Eye tracking research & applications
Averaging scan patterns and what they can tell us
Proceedings of the 2006 symposium on Eye tracking research & applications
The influence of web browsing experience on web-viewing behavior
Proceedings of the 2006 symposium on Eye tracking research & applications
The influence of task and gender on search and evaluation behavior using Google
Information Processing and Management: an International Journal
What are you looking for?: an eye-tracking study of information usage in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Testing for statistically significant differences between groups of scan patterns
Proceedings of the 2008 symposium on Eye tracking research & applications
Validating the use and role of visual elements of web pages in navigation with an eye-tracking study
Proceedings of the 17th international conference on World Wide Web
What do you see when you're surfing?: using eye tracking to predict salient regions of web pages
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Generation Y, web design, and eye tracking
International Journal of Human-Computer Studies
Older adults and the web: lessons learned from eye-tracking
UAHCI'07 Proceedings of the 4th international conference on Universal access in human computer interaction: coping with diversity
Combining eye tracking and conventional techniques for indications of user-adaptability
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
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Eye-tracking studies have provided us with interesting findings regarding the way users explore Web pages. Usually, visual fixations are represented using gaze plots and heatmaps. Probably one of the most cited studies on Web page exploration is the one by Nielsen [15] who demonstrated that users often read Web pages in an F-shaped pattern. However, this conclusion is based on aggregated data that do not represent any real user. In order to characterize and to compare scanpaths so as to uncover possible scan-patterns, a clustering method based on the Hausdorff distance has been applied to the data from 113 users. The results have shown that scan-patterns could be identified. Groups of users have been identified and their behaviors have been described with diverse eye-tracking metrics. The contributions of this study are outlined as well as its drawbacks. Research perspectives are proposed.