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Article

Epistemic Network Analyses of Economics Students’ Graph Understanding: An Eye-Tracking Study

1
Department of Business and Economics Education, Johannes Gutenberg-University Mainz, 55128 Mainz, Germany
2
Information Center for Education, DIPF Leibniz Institute for Research and Information in Education, 60323 Frankfurt Am Main, Germany
3
Computer Science Faculty, Goethe University, 60323 Frankfurt am Main, Germany
4
Educational Science Faculty, Open University of the Netherlands, 6419 AT Heerlen, The Netherlands
*
Authors to whom correspondence should be addressed.
Both authors contributed equally to the development of the paper.
Sensors 2020, 20(23), 6908; https://doi.org/10.3390/s20236908
Received: 12 October 2020 / Revised: 24 November 2020 / Accepted: 27 November 2020 / Published: 3 December 2020
(This article belongs to the Special Issue From Sensor Data to Educational Insights)
Learning to solve graph tasks is one of the key prerequisites of acquiring domain-specific knowledge in most study domains. Analyses of graph understanding often use eye-tracking and focus on analyzing how much time students spend gazing at particular areas of a graph—Areas of Interest (AOIs). To gain a deeper insight into students’ task-solving process, we argue that the gaze shifts between students’ fixations on different AOIs (so-termed transitions) also need to be included in holistic analyses of graph understanding that consider the importance of transitions for the task-solving process. Thus, we introduced Epistemic Network Analysis (ENA) as a novel approach to analyze eye-tracking data of 23 university students who solved eight multiple-choice graph tasks in physics and economics. ENA is a method for quantifying, visualizing, and interpreting network data allowing a weighted analysis of the gaze patterns of both correct and incorrect graph task solvers considering the interrelations between fixations and transitions. After an analysis of the differences in the number of fixations and the number of single transitions between correct and incorrect solvers, we conducted an ENA for each task. We demonstrate that an isolated analysis of fixations and transitions provides only a limited insight into graph solving behavior. In contrast, ENA identifies differences between the gaze patterns of students who solved the graph tasks correctly and incorrectly across the multiple graph tasks. For instance, incorrect solvers shifted their gaze from the graph to the x-axis and from the question to the graph comparatively more often than correct solvers. The results indicate that incorrect solvers often have problems transferring textual information into graphical information and rely more on partly irrelevant parts of a graph. Finally, we discuss how the findings can be used to design experimental studies and for innovative instructional procedures in higher education. View Full-Text
Keywords: epistemic network analysis; eye-tracking; graph understanding; economics; higher education epistemic network analysis; eye-tracking; graph understanding; economics; higher education
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MDPI and ACS Style

Brückner, S.; Schneider, J.; Zlatkin-Troitschanskaia, O.; Drachsler, H. Epistemic Network Analyses of Economics Students’ Graph Understanding: An Eye-Tracking Study. Sensors 2020, 20, 6908. https://doi.org/10.3390/s20236908

AMA Style

Brückner S, Schneider J, Zlatkin-Troitschanskaia O, Drachsler H. Epistemic Network Analyses of Economics Students’ Graph Understanding: An Eye-Tracking Study. Sensors. 2020; 20(23):6908. https://doi.org/10.3390/s20236908

Chicago/Turabian Style

Brückner, Sebastian, Jan Schneider, Olga Zlatkin-Troitschanskaia, and Hendrik Drachsler. 2020. "Epistemic Network Analyses of Economics Students’ Graph Understanding: An Eye-Tracking Study" Sensors 20, no. 23: 6908. https://doi.org/10.3390/s20236908

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