An exploratory study using social network analysis to model eye movements in mathematics problem solving

Abstract

Eye tracking is a useful tool to understand students' cognitive process during problem solving. This paper offers a unique perspective by applying techniques from social network analysis to eye movement patterns in mathematics problem solving. We construct and visualize transition networks using eye-tracking data collected from 37 8th grade students while solving linear function problems. By applying network analysis on the constructed transition networks, we find general transition patterns between areas of interest (AOIs) for all students, and we also compare patterns for high- and low-performing students. Our results show that even though students share general transition patterns during problem solving, high-performing students made more strategic transitions among AOI triples than low-performing students.