Modeling Reading Behaviors: An Automatic Approach to Eye Movement Analytics

Abstract

Critical reading plays an important role in science learning, and previous studies have endeavored to objectively and precisely capture readers’ cognitive processing in reading scientific texts. Since many factors affect readers’ initiation and comprehension of scientific texts, studying the interactions of these factors was technically challenging for earlier studies. Recently, the use of artificial intelligent techniques for analyzing physiological signals has gained significant research attention, but exploitation of the educational data for proactive instructional use is still limited. This study proposed and evaluated an automatic approach incorporating the K-means++ clustering method for eye movement analytics. In this study, 64 undergraduate and graduate students read a multi-page popular science text while their eye movements were recorded. The results of the cluster analysis identified three patterns of reading behavior that were consistent and comparable to those of previous studies using self-reported measures and post-analysis analytics. Findings of the study support the potential and validity of a bottom-up, data-driven approach that can directly examine and analyze reading behaviors without interruption, and the contribution of the study to research and practice is outlined.