Identifying Reading Patterns with Eye-tracking Visual Analytics

Abstract

Eye-tracking studies can yield insight into patterns of reading strategies, but identifying patterns in eye-tracking visualizations is a cognitively demanding task. My dissertation explores how visual analytics approaches support analysts detecting sequential patterns in the eye-tracking data. To demonstrate the effectiveness of our visual analytics, I apply it to the datasets from a series of eye-tracking studies, and gather an empirical understanding about how research articles are read on paper and other media.