A Practical Guide to Usability Testing
A Practical Guide to Usability Testing
Cognitive strategies and eye movements for searching hierarchical computer displays
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Eye Tracking Methodology: Theory and Practice
Eye Tracking Methodology: Theory and Practice
Automated usability testing framework
AUIC '08 Proceedings of the ninth conference on Australasian user interface - Volume 76
Handbook of Usability TestingXXX: Howto Plan, Design, and Conduct Effective Tests
Handbook of Usability TestingXXX: Howto Plan, Design, and Conduct Effective Tests
Low-cost gaze interaction: ready to deliver the promises
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Real-time eye gaze tracking with an unmodified commodity webcam employing a neural network
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Are engineers condemned to design? a survey on software engineering and UI design in Switzerland
INTERACT'07 Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction - Volume Part II
Aiding usability evaluation via detection of excessive visual search
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Changing perspectives on evaluation in HCI: past, present, and future
CHI '13 Extended Abstracts on Human Factors in Computing Systems
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Automated detection of excessive visual search (ES) experienced by a user during software use presents the potential for substantial improvement in the efficiency of supervised usability analysis. This paper presents an objective evaluation of several methods for the automated segmentation and classification of ES intervals from an eye movement recording, a technique that can be utilized to aid in the identification of usability problems during software usability testing. Techniques considered for automated segmentation of the eye movement recording into unique intervals include mouse/keyboard events and eye movement scanpaths. ES is identified by a number of eye movement metrics, including: fixation count, saccade amplitude, convex hull area, scanpath inflections, scanpath length, and scanpath duration. The ES intervals identified by each algorithm are compared to those produced by manual classification to verify the accuracy, precision, and performance of each algorithm. The results indicate that automated classification can be successfully employed to substantially reduce the amount of recorded data reviewed by HCI experts during usability testing, with relatively little loss in accuracy.