Automatic detection of good and bad usability in web forms using eye-tracking-data

Abstract

An established method for detecting usability problems is the analysis of user gaze behavior with an eye-tracker. These studies require a significant amount of time and effort to evaluate the data. An automated detection of good and bad usability in recorded user data can support usability experts in eye-tracking evaluation and reduce the effort. In the present work, suitable eye-tracking metrics are presented that correlate with the quality of usability. A quantitative A/B-user-study with eye-tracking was conducted which recorded gaze behavior of 30 subjects filling out a web form. The web form was designed in such a way that each form page was available as a good and bad variant according to known usability guidelines. The results confirm a significant correlation between the eye-tracking metric visits to an AOI and the usability. The eye-tracking metrics number of fixations within an AOI and duration of fixations within an AOI also correlate with the quality of usability. No correlation could be confirmed for the time of first fixation within an AOI.