Classification of reading and not reading behavior based on eye movement analysis

Abstract

Nowadays, many researchers analyze reading behavior with eye trackers. Various traits of reading like engagement, or text difficulty have been observed in laboratory settings. But, their automatic application for daily life is usually prevented by one question: when is somebody reading? We have developed a tool to classify short sequences of fixations from eye gaze data into reading and not reading. Our specific use case is the Vocabulometer, a website for learning English by reading texts. We used supervised learning on data from nonnative English speakers to train decision trees for the classification. With features based on vertical eye movement, we achieved 93.1% of correct classifications.