Assessing Map-Reading Skills Using Eye Tracking and Bayesian Structural Equation Modelling

Abstract

Map reading is an important skill for acquiring spatial information. Previous studies have mainly used results-based assessments to learn about map-reading skills. However, how to model the relationship between map-reading skills and eye movement metrics is not well documented. In this paper, we propose a novel method to assess map-reading skills using eye movement metrics and Bayesian structural equation modelling. We recruited 258 participants to complete five map-reading tasks, which included map visualization, topology, navigation, and spatial association. The results indicated that map-reading skills could be reflected in three selected eye movement metrics, namely, the measure of first fixation, the measure of processing, and the measure of search. The model fitted well for all five tasks, and the scores generated by the model reflected the accuracy and efficiency of the participants’ performance. This study might provide a new approach to facilitate the quantitative assessment of map-reading skills based on eye tracking.