Usability engineering turns 10
interactions
The state of the art in automating usability evaluation of user interfaces
ACM Computing Surveys (CSUR)
Visual attention to repeated internet images: testing the scanpath theory on the world wide web
ETRA '02 Proceedings of the 2002 symposium on Eye tracking research & applications
Common industry format for usability test reports
CHI '00 Extended Abstracts on Human Factors in Computing Systems
The determinants of web page viewing behavior: an eye-tracking study
Proceedings of the 2004 symposium on Eye tracking research & applications
Eye-tracking analysis of user behavior in WWW search
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Averaging scan patterns and what they can tell us
Proceedings of the 2006 symposium on Eye tracking research & applications
openEyes: a low-cost head-mounted eye-tracking solution
Proceedings of the 2006 symposium on Eye tracking research & applications
eyePatterns: software for identifying patterns and similarities across fixation sequences
Proceedings of the 2006 symposium on Eye tracking research & applications
Identifying web usability problems from eye-tracking data
BCS-HCI '07 Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI...but not as we know it - Volume 1
The good, the bad, and the random: an eye-tracking study of ad quality in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
EyePhone: activating mobile phones with your eyes
Proceedings of the second ACM SIGCOMM workshop on Networking, systems, and applications on mobile handhelds
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Eye tracking has been around for more than 100 years and the technology has improved at an incredible rate. With the advancement of technology, eye tracking can even be done from a mobile phone, which allows for large scale eye tracking studies to be performed. Unfortunately, eye tracking analysis is still a time consuming activity especially when done on a large scale, due to the high dependence on human expertise. This paper introduces saccade deviation indices (SDI) and saccade length indices (SLI), metrics to assist in faster analysis of eye tracking data. In addition, bench-mark deviation vectors (BDV) are introduced to highlight repetitive path deviation in eye tracking data. In order to obtain these metrics, a benchmark user is used to determine where and by how much the participants deviated from the expected scan path. A study was performed, recording the eye movements of participants while using a mobile procurement application. The results were compared to the results of an expert usability study to establish the feasibility of this approach. Preliminary results indicate that the SDI and SLI can reduce the time that an experts spend analysing eye tracking data. Additional time is saved by highlighting possible usability issues, by mapping BDV back onto the user interfaces, indicating where the user deviated from the expected scan path.