Changes in Students’ Understanding of and Visual Attention on Digitally Represented Graphs Across Two Domains in Higher Education: A Postreplication Study

Abstract

Domain-specific understanding of digitally represented graphs is necessary for successful learning within and across domains in higher education. Two recent studies conducted a cross-sectional analysis of graph understanding in different contexts (physics and finance), task concepts, and question types among students of physics, psychology, and economics. However, neither changes in graph processing nor changes in test scores over the course of one semester have been sufficiently researched so far. This eye-tracking replication study with a pretest–posttest design examines and contrasts changes in physics and economics students’ understanding of linear physics and finance graphs. It analyzes the relations between changes in students’ gaze behavior regarding relevant graph areas, scores, and self-reported task-related confidence. The results indicate domain-specific, context- and concept-related differences in the development of graph understanding over the first semester, as well as its successful transferability across the different contexts and concepts. Specifically, we discovered a tendency of physics students to develop a task-independent overconfidence in the graph understanding during the first semester.

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

“…In recent studies, eye-tracking was used to get a closer look at students' response processes [1,3,20] to get an indication of the difficulties of graph understanding. This method enables the analysis of not only the test scores but allows researchers to determine with which frequency and sequence a learner looks at individual parts of a line graph-so-termed Areas of Interest (AOIs)-during the process of task solving (e.g., axis labels, axis tick labels, response options like the attractor or distractor, the curve).…”

“…Studies that examined the understanding of line graphs in physics and economics contexts considered the dwell times and fixation times on AOIs of a graph and suggested that certain eye-tracking metrics provide initial indications of students' learning difficulties [1,20]. Relevant AOIs (i.e., areas containing critical information, e.g., axes) were viewed longer and with higher frequency by physics students who show a higher understanding of graphs [1,3].…”

“…The few existing studies address merely the instructional quality of financial graphs in textbooks [16], the construction principles of graphs [11], and potential factors influencing the use of graphs in instruction [19]. Whether the comprehensive findings on graph understanding, in particular from physics education research, can be transferred and applied in the context of economics and finance is under-researched [20]. Previous studies focusing on the contexts of economics and physics indicated differences in graph understanding at the level of individual tasks concerning the graph concepts 'area under a curve' and 'slope of a curve' and the task's level of mathematical requirements [1,3,20].…”

“…Whether the comprehensive findings on graph understanding, in particular from physics education research, can be transferred and applied in the context of economics and finance is under-researched [20]. Previous studies focusing on the contexts of economics and physics indicated differences in graph understanding at the level of individual tasks concerning the graph concepts 'area under a curve' and 'slope of a curve' and the task's level of mathematical requirements [1,3,20]. Since the transfer of graph understanding between contexts seems to be difficult [21], there is a need for more studies that examine how the knowledge and skills acquired in one domain are applied to graph tasks from different domains in a comparative way.…”

“…Due to technological developments, a large number of recent studies, especially in the natural sciences, used eye-tracking to observe the process of graph understanding [15,22,23]. Current eye-tracking studies mainly examine students' dwell time, frequency or duration of fixations on individual graph components [1,3,20]. These analyses, however, reflect only an isolated part of students' graph task solution behavior.…”