The reading assistant: eye gaze triggered auditory prompting for reading remediation
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
An eye tracking study of how pictures influence online reading
INTERACT'07 Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction - Volume Part II
Mouse tracking: measuring and predicting users' experience of web-based content
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Finding and exploring memes in social media
Proceedings of the 23rd ACM conference on Hypertext and social media
Robust models of mouse movement on dynamic web search results pages
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We analyze gaze patterns to study how users in online reading environments cope with visual distraction, and we report gaze markers that identify reading difficulties due to distraction. The amount of visual distraction is varied from none, medium to high by presenting irrelevant graphics beside the reading content in one of 3 conditions: no graphic, static or animated graphics. We find that under highly-distracting conditions, a struggling reader puts more effort into the text -- she takes a longer time to comprehend the text, performs more fixations on the text and frequently revisits previously read content. Furthermore, she reports an unpleasant reading experience. Interestingly, we find that whether the user is distracted and struggles or not can be predicted from gaze patterns alone with up to 80% accuracy and up to 15% better than with non-gaze based features. This suggests that gaze patterns can be used to detect key events such as user strugglefrustration while reading.