Optimizing user interfaces in food production: gaze tracking is more sensitive for A-B-testing than behavioral data alone
Daniel Walper, Julia Kassau, Philipp Methfessel, Timo Pronold, Wolfgang Einhäuser · ACM Symposium on Eye Tracking Research and Applications · 2020
Eye-tracking data often provide access to information about users’ strategies and preferences that extend beyond purely behavioral data. Thanks to modern eye-tracking technology, gaze can be tracked rather unobtrusively in real-world settings. Here we examine the usefulness of gaze tracking with a mobile eye-tracker for interface design in an industrial setting, specifically the operation of a food production line. We use a mock task that is similar in its interface usage to the actual production task in routine machine operation. We compare two interface designs to each other as well as two levels of user expertise. We do not find any effects of experience or interface type in the behavioral data - in particular, both user groups needed the same time to complete the task on average. However, gaze data reveals different strategies: users with high experience in using the interface spend significantly less time looking at the screen – that is, actually interacting with the interface – in absolute terms as well as expressed as fraction of the total time needed to complete the task. This exemplifies how gaze tracking can be utilized to uncover different user-dependent strategies that would not be accessible through behavioral data alone.