Do Graph Readers Prefer the Graph Type Most Suited to a Given Task? Insights from Eye Tracking

  • Benjamin Strobel Leibniz Institute for Science and Mathematics Education (IPN), Kiel
  • Steffani Saß Leibniz Institute for Science and Mathematics Education (IPN), Kiel
  • Marlit Annalena Lindner Leibniz Institute for Science and Mathematics Education (IPN), Kiel
  • Olaf Köller Leibniz Institute for Science and Mathematics Education (IPN), Kiel
Keywords: Graph comprehension, graph preference, diagrams, linear mixed-effects models, eye tracking

Abstract

Research on graph comprehension suggests that point differences are easier to read in bar graphs, while trends are easier to read in line graphs. But are graph readers able to detect and use the most suited graph type for a given task? In this study, we applied a dual repre-sentation paradigm and eye tracking methodology to determine graph readers’ preferential processing of bar and line graphs while solving both point difference and trend tasks. Data were analyzed using linear mixed-effects models. Results show that participants shifted their graph preference depending on the task type and refined their preference over the course of the graph task. Implications for future research are discussed.

Author Biographies

Benjamin Strobel, Leibniz Institute for Science and Mathematics Education (IPN), Kiel
Department of Educational Research / Educational Assessment and Measurement
Steffani Saß, Leibniz Institute for Science and Mathematics Education (IPN), Kiel
Department of Educational Research / Educational Assessment and Measurement
Marlit Annalena Lindner, Leibniz Institute for Science and Mathematics Education (IPN), Kiel
Department of Educational Research / Educational Assessment and Measurement
Olaf Köller, Leibniz Institute for Science and Mathematics Education (IPN), Kiel
Department of Educational Research / Educational Assessment and Measurement
Published
04-07-2016
How to Cite
Strobel, B., Saß, S., Lindner, M., & Köller, O. (2016). Do Graph Readers Prefer the Graph Type Most Suited to a Given Task? Insights from Eye Tracking. Journal of Eye Movement Research, 9(4). https://doi.org/10.16910/jemr.9.4.4
Section
Articles