Teaching Eye Tracking Visual Analytics in Computer and Data Science Bachelor Courses

Abstract

Making students aware of eye tracking technologies can have a great benefit on the entire application field since they may build the next generation of eye tracking researchers. On the one hand students learn the usefulness and benefits of this technique for different scientific purposes like user evaluation to find design flaws or visual attention strategies, gaze-assisted interaction to enhance and augment traditional interaction techniques, or as a means to improve virtual reality experiences. However, on the other hand, the large amount of recorded data means a challenge for data analytics in order to find rules, patterns, but also anomalies in the data, finally leading to insights and knowledge to understand or predict eye movement patterns which can have synergy effects for both disciplines - eye tracking and visual analytics. In this paper we will describe the challenges of teaching eye tracking combined with visual analytics in a computer and data science bachelor course with 42 students in an active learning scenario following four teaching stages. Some of the student project results are shown to demonstrate learning outcomes with respect to eye tracking data analysis and visual analytics techniques.