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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
The digital and information age has fundamentally transformed the way in which students learn and the study material they have at their disposal, especially in higher education. Students need to possess a number of higher-order cognitive and metacognitive skills, including effective information processing and critical reasoning to be able to navigate the Internet and use online sources, even those found outside of academically curated domains and in the depths of the Internet, and to solve (domain-specific) problems. Linking qualitative and quantitative research and connecting the humanities to empirical educational science studies, this article investigates the role of narratives and their impact on university students’ information seeking and their critical online reasoning (COR). This study focuses on the link between students’ online navigation skills, information seeking behavior and critical reasoning with regard to the specific domains: economics and medicine. For the empirical analysis in this article, we draw on a study that assesses the COR skills of undergraduate students of economics and medicine at two German universities. To measure COR skills, we used five tasks from the computer-based assessment “Critical Online Reasoning Assessment” (CORA), which assesses students’ skills in critically evaluating online sources and reasoning using evidence on contentious issues. The conceptual framework of this study is based on an existing methodology – narrative economics and medicine – and discusses its instructional potential and how it can be used to develop a new tool of “wise interventions” to enhance students’ COR in higher education. Based on qualitative content analyses of the students’ written responses, i.e., short essays, three distinct patterns of information seeking behavior among students have been identified. These three patterns – “Unambiguous Fact-Checking,” “Perspective-Taking Without Fact-Checking,” and “Web Credibility-Evaluating” – differ substantially in their potential connection to underlying narratives of information used by students to solve the CORA tasks. This analysis suggests that training university students in narrative analysis can strongly contribute to enhancing their critical online reasoning.
To successfully learn using open Internet resources, students must be able to critically search, evaluate and select online information, and verify sources. Defined as critical online reasoning (COR), this construct is operationalized on two levels in our study: (1) the student level using the newly developed Critical Online Reasoning Assessment (CORA), and (2) the online information processing level using event log data, including gaze durations and fixations. The written responses of 32 students for one CORA task were scored by three independent raters. The resulting score was operationalized as “task performance,” whereas the gaze fixations and durations were defined as indicators of “process performance.” Following a person-oriented approach, we conducted a process mining (PM) analysis, as well as a latent class analysis (LCA) to test whether—following the dual-process theory—the undergraduates could be distinguished into two groups based on both their process and task performance. Using PM, the process performance of all 32 students was visualized and compared, indicating two distinct response process patterns. One group of students (11), defined as “strategic information processers,” processed online information more comprehensively, as well as more efficiently, which was also reflected in their higher task scores. In contrast, the distributions of the process performance variables for the other group (21), defined as “avoidance information processers,” indicated a poorer process performance, which was also reflected in their lower task scores. In the LCA, where two student groups were empirically distinguished by combining the process performance indicators and the task score as a joint discriminant criterion, we confirmed these two COR profiles, which were reflected in high vs. low process and task performances. The estimated parameters indicated that high-performing students were significantly more efficient at conducting strategic information processing, as reflected in their higher process performance. These findings are so far based on quantitative analyses using event log data. To enable a more differentiated analysis of students’ visual attention dynamics, more in-depth qualitative research of the identified student profiles in terms of COR will be required.