An Exploratory Study Using Innovative Graphical Network Analysis to Model Eye Movements in Spatial Reasoning Problem Solving

Abstract Eye‐tracking procedures generate copious process data that could be valuable in establishing the response processes component of modern validity theory. However, there is a lack of tools for assessing and visualizing response processes using process data such as eye‐tracking fixation sequences, especially those suitable for young children. This study, which explored student responses to a spatial reasoning task, employed eye tracking and social network analysis to model, examine, and visualize students' visual transition patterns while solving spatial problems to begin to elucidate these processes. Fifty students in Grades 2–8 completed a spatial reasoning task as eye movements were recorded. Areas of interest (AoIs) were defined within the task for each spatial reasoning question. Transition networks between AoIs were constructed and analyzed using selected network measures. Results revealed shared transition sequences across students as well as strategic differences between high and low performers. High performers demonstrated more integrated transitions between AoIs, while low performers considered information more in isolation. Additionally, age and the interaction of age and performance did not significantly impact these measures. The study demonstrates a novel modeling approach for investigating visual processing and provides initial evidence that high‐performing students more deeply engage with visual information in solving these types of questions.