An algorithmic gaze based approach for the measurement of expertise development

In our latest work we investigate how we can measure the development of visual expertise over time using eye tracking technology. We introduce an algorithmic approach for the extraction of object-related gaze sequences and determine task-related expertise by investigating the development of gaze sequence patterns during a multi-trial study of a simplified airplane assembly task. By using k-mers, a well known method from the field of computational biology, we were able to show that with increased on-task experience, novices show a significant increase of expert gaze patterns. The results illustrate that the multi-trial k-mer approach is suitable for revealing specific cognitive processes and can quantify learning progress using gaze patterns that include both spatial and temporal information, which could provide a valuable tool for novice training and expert assessment.