TY - JOUR
A1 - Brückner, Sebastian
A1 - Schneider, Jan
A1 - Zlatkin-Troitschanskaia, Olga
A1 - Drachsler, Hendrik
T1 - Epistemic network analyses of economics students’ graph understanding: an eye-tracking study
T2 - Sensors
N2 - 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
KW - epistemic network analysis
KW - eye-tracking
KW - graph understanding
KW - economics
KW - higher education
Y1 - 2020
UR - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/57524
UR - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-575248
SN - 1424-8220
VL - 20
IS - Article 6908
PB - MDPI
CY - Basel
ER -