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

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  • Weihua Dong

    (Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Research Center of Geospatial Cognition and Visual Analytics and Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Yuhao Jiang

    (Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Research Center of Geospatial Cognition and Visual Analytics and Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Liangyu Zheng

    (Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Research Center of Geospatial Cognition and Visual Analytics and Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Bing Liu

    (Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Research Center of Geospatial Cognition and Visual Analytics and Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Liqiu Meng

    (Chair of Cartography, Technical University of Munich, 80333 Munich, Germany)

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.

Suggested Citation

  • Weihua Dong & Yuhao Jiang & Liangyu Zheng & Bing Liu & Liqiu Meng, 2018. "Assessing Map-Reading Skills Using Eye Tracking and Bayesian Structural Equation Modelling," Sustainability, MDPI, vol. 10(9), pages 1-13, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3050-:d:166135
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    References listed on IDEAS

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    1. Coltman, Tim & Devinney, Timothy M. & Midgley, David F. & Venaik, Sunil, 2008. "Formative versus reflective measurement models: Two applications of formative measurement," Journal of Business Research, Elsevier, vol. 61(12), pages 1250-1262, December.
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    1. Yu-Ling Hsieh & Ming-Feng Lee & Guey-Shya Chen & Wei-Jie Wang, 2022. "Application of Visitor Eye Movement Information to Museum Exhibit Analysis," Sustainability, MDPI, vol. 14(11), pages 1-15, June.

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