Eye movement measurement provides spatiotemporal information about students’ visual attention during a given activity. It is commonly used to investigate problem solving in various science education studies. In two studies reported here, we used eye tracking to investigate students’ understanding of line diagrams in different contexts, as this is an important skill necessary for understanding information in science and everyday life that is often conveyed through diagrams. In doing so, we compared the competencies of physics, psychology, and business students on problems related to the slope of graphs and the area under the graph. Comparisons between experts (physics students) and non-experts (psychology and economics students) in their subject area (physics) and in another subject area (finance) with isomorphic pairs of questions allow us to estimate the transfer of competence from one subject area to another. The results show that physics students perform better than non-physics students in all concepts, but still have difficulty transferring their performance to non-physics problems. In addition to student scores and total time spent, eye-tracking provides information about the time spent on different parts of the graph. A difference heatmap is introduced, showing the difference between the physics and finance questions in visual attention for physics, psychology, and economics students. The heatmaps provide insight into the transfer of knowledge from physics to a new context, such as finance, and allow more detailed comparisons of the patterns of visual attention of experts and non-experts. Implications of our results for teaching and learning about graphs in mathematics and science courses are discussed.
This is a preview of subscription content, access via your institution.
More specifications can be found on the product website https://www.tobiipro.com.
Beichner, R. J. (1994). Testing student interpretation of kinematics graphs. American Journal of Physics, 62(8), 750–762.
Bollen, L., De Cock, M., Zuza, K., Guisasola, J., & van Kampen, P. (2016). Generalizing a categorization of students’ interpretations of linear kinematics graphs. Physical Review Physics Education Research, 12(1), 010108.
Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. Review of Research in Education, 24, 61–100.
Carpenter, P. A., & Shah, P. (1998). A model of the perceptual and conceptual processes in graph comprehension. Journal of Experimental Psychology: Applied, 4(2), 75.
Christensen, W. M., & Thompson, J. R. (2012). Investigating graphical representations of slope and derivative without a physics context. Physical Review Special Topics-Physics Education Research, 8(2), 023101.
Curcio, F. R. (1987). Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics Education, 18, 382–393.
Freedman, E. G., & Shah, P. (2002, April). Toward a model of knowledge-based graph comprehension. In International conference on theory and application of diagrams (pp. 18–30). Berlin, Heidelberg: Springer.
Gegenfurtner, A., Lehtinen, E., & Säljö, R. (2011). Expertise differences in the comprehension of visualizations: A meta-analysis of eye-tracking research in professional domains. Educational Psychology Review, 23(4), 523–552.
Goldberg, J., & Helfman, J. (2011). Eye tracking for visualization evaluation: Reading values on linear versus radial graphs. Information visualization, 10(3), 182–195.
Hammer, D., Elby, A., Scherr, R. E., & Redish, E. F. (2005). Resources, framing, and transfer. In Transfer of learning from a modern multidisciplinary perspective (pp. 89–120). Greenwich: Information Age Publishing.
Hoban, R. A., Finlayson, O. E., & Nolan, B. C. (2013). Transfer in chemistry: A study of students’ abilities in transferring mathematical knowledge to chemistry. International Journal of Mathematical Education in Science and Technology, 44(1), 14–35.
Ivanjek, L., Planinic, M., Hopf, M., & Susac, A. (2017). Student difficulties with graphs in different contexts. In Cognitive and affective aspects in science education research (pp. 167–178). Cham: Springer.
Ivanjek, L., Susac, A., Planinic, M., Andrasevic, A., & Milin-Sipus, Z. (2016). Student reasoning about graphs in different contexts. Physical Review Physics Education Research, 12(1), 010106.
Kekule, M. (2014). Students’ approaches when dealing with kinematics graphs explored by eye-tracking research method. In Proceedings of the frontiers in mathematics and science education research conference, FISER (pp. 108–117).
Kekule, M. (2015). Students’ different approaches to solving problems from kinematics in respect of good and poor performance. In International Conference on Contemporary Issues in Education, ICCIE (pp. 126–134).
Klein, P., Küchemann, S., Brückner, S., Zlatkin-Troitschanskaia, O., & Kuhn, J. (2019). Student understanding of graph slope and area under a curve: A replication study comparing first-year physics and economics students. Physical Review Physics Education Research, 15(2),
Kozhevnikov, M., Motes, M. A., & Hegarty, M. (2007). Spatial visualization in physics problem solving. Cognitive science, 31(4), 549–579.
Leinhardt, G., Zaslavsky, O., & Stein, M. K. (1990). Functions, graphs, and graphing: Tasks, learning, and teaching. Review of Educational Research, 60, 1.
Madsen, A. M., Larson, A. M., Loschky, L. C., & Rebello, N. S. (2012). Differences in visual attention between those who correctly and incorrectly answer physics problems. Physical Review Special Topics-Physics Education Research, 8(1), 010122.
Madsen, A., Rouinfar, A., Larson, A. M., Loschky, L. C., & Rebello, N. S. (2013). Can short duration visual cues influence students’ reasoning and eye movements in physics problems? Physical Review Special Topics-Physics Education Research, 9(2), 020104.
McDermott, L. C., Rosenquist, M. L., & Van Zee, E. H. (1987). Student difficulties in connecting graphs and physics: Examples from kinematics. American Journal of Physics, 55(6), 503–513.
Pinker, S. (1990). A theory of graph comprehension. In R. Freedle (Ed.), Artificial intelligence and the future of testing (pp. 73–126). Hillsdale, NJ: Erlbaum.
Planinic, M., Ivanjek, L., Susac, A., & Milin-Sipus, Z. (2013). Comparison of university students’ understanding of graphs in different contexts. Physical Review Special Topics-Physics Education Research, 9(2), 020103.
Planinic, M., Milin-Sipus, Z., Katic, H., Susac, A., & Ivanjek, L. (2012). Comparison of student understanding of line graph slope in physics and mathematics. International Journal of Science and Mathematics Education, 10(6), 1393–1414.
Potgieter, M., Harding, A., & Engelbrecht, J. (2008). Transfer of algebraic and graphical thinking between mathematics and chemistry. Journal of Research in Science Teaching, 45(2), 197–218.
Salvucci, D. D., & Goldberg, J. H. (2000, November). Identifying fixations and saccades in eye-tracking protocols. In Proceedings of the 2000 symposium on Eye tracking research & applications (pp. 71–78).
Strobel, B., Lindner, M. A., Saß, S., & Köller, O. (2018). Task-irrelevant data impair processing of graph reading tasks: An eye tracking study. Learning and Instruction, 55, 139–147.
Susac, A., Bubic, A., Kazotti, E., Planinic, M., & Palmovic, M. (2018). Student understanding of graph slope and area under a graph: A comparison of physics and nonphysics students. Physical Review Physics Education Research, 14(2), 020109.
Viiri, J., Kekule, M., Isoniemi, J., & Hautala, J. (2017). Eye-tracking the effects of representation on students’ problem solving approaches. In Proceedings of the FMSERA annual symposium. Finnish Mathematics and Science Education Research Association (FMSERA).
Wemyss, T., & van Kampen, P. (2013). Categorization of first-year university students’ interpretations of numerical linear distance-time graphs. Physical Review Special Topics-Physics Education Research, 9(1), 010107.
Zavala, G., Tejeda, S., Barniol, P., & Beichner, R. J. (2017). Modifying the test of understanding graphs in kinematics. Physical Review Physics Education Research, 13(2), 020111.
Editors and Affiliations
Rights and permissions
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Klein, P. et al. (2021). Students’ Understanding of Diagrams in Different Contexts: Comparison of Eye Movements Between Physicists and Non-physicists Using Eye-Tracking. In: Devetak, I., Glažar, S.A. (eds) Applying Bio-Measurements Methodologies in Science Education Research. Springer, Cham. https://doi.org/10.1007/978-3-030-71535-9_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71534-2
Online ISBN: 978-3-030-71535-9
eBook Packages: EducationEducation (R0)