Threshold Concepts for Modeling and Assessing Higher Education Students’ Understanding and Learning in Economics

Brückner, Sebastian; Zlatkin‐Troitschanskaia, Olga

doi:10.1007/978-3-319-74338-7_6

Cited by 9 publications

(19 citation statements)

References 21 publications

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“…This effect increased to greater than 60% at t2. This finding is also in line with numerous studies across disciplines (Nowell and Alston, 2007;Bell and Volckman, 2011;Guest and Riegler, 2017;Brückner and Zlatkin-Troitschanskaia, 2018).…”

Section: Change In Confidence Across Contexts and Domainssupporting

confidence: 92%

“…The metacognitive assessment provided further significant insights into the students' expertise, in particular between high-and low-performing students. In line with this existing research, Klein et al (2019) found better selfassessments among high-performing students (Brückner and Zlatkin-Troitschanskaia, 2018) and a systematic overestimation of their own abilities among low-performing students (Kruger and Dunning, 1999). The physics students provided correct answers with higher confidence ratings in comparison to instances when they gave incorrect answers, whereas economics students who achieved lower scores also gave lower confidence ratings with regard to their own performance.…”

Section: Background Of the Post-replication Studysupporting

confidence: 73%

“…While Klein et al (2019) showed the connection between the self-assessment of solutions and the correct answers to these graph tasks, there are hardly any studies that investigate this relationship over time. However, prior, longitudinal research has identified correlations of this kind in studies using general knowledge tests (without graphs; Cordova et al, 2014;Brückner and Zlatkin-Troitschanskaia, 2018). Moreover, the Dunning-Kruger effect (Kruger and Dunning, 1999) suggests that learners with a low level of knowledge struggle to rate their own performance accurately in self-assessments.…”

Section: Development Of Graph Understandingmentioning

confidence: 99%

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

Brückner

Zlatkin‐Troitschanskaia

Küchemann

et al. 2020

Front. Psychol.

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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.

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Section: Change In Confidence Across Contexts and Domainssupporting

confidence: 92%

Section: Background Of the Post-replication Studysupporting

confidence: 73%

Section: Development Of Graph Understandingmentioning

confidence: 99%

See 1 more Smart Citation

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

Brückner

Zlatkin‐Troitschanskaia

Küchemann

et al. 2020

Front. Psychol.

Self Cite

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“…However, the physics students' average confidence rating for incorrect answers is still about 50%, revealing potential to improve their metacognitive skills. Several investigations found that the accuracy of confidence judgments varies among different ability levels; viz., low performing students do not reliably distinguish between correct and incorrect responses [9,11,34,35]. This phenomenon is well studied in educational psychology and is called the Dunning-Kruger effect [13].…”

Section: Discussionmentioning

confidence: 99%

“…Besides the evaluation of standard test scores, the rating of confidence levels with multiple-choice questions recently became popular in physics education research (PER) [8][9][10]. From studies with economics and psychology students, it is known that high-performing students have a better ability to accurately judge their own performance [11]. This observation can be quantified by comparing the confidence ratings associated with correct responses with those of incorrect responses [12].…”

Section: Introductionmentioning

confidence: 99%

Student understanding of graph slope and area under a curve: A replication study comparing first-year physics and economics students

Klein

Küchemann

Brückner

et al. 2019

Phys. Rev. Phys. Educ. Res.

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The understanding of graphs and extraction of relevant information from graphs plays a major role in physics education and is also important in several related fields. Recently, Susac et al. [Phys. Rev. Phys. Educ. Res. 14, 020109 (2018)] compared physics and psychology students' understanding of graphs in the contexts of physics and finance. They showed that physicists scored significantly higher in both domains and that all students solved the slope problems better than the area problems. Moreover, eye-tracking data revealed that physics students spent more time on problems associated with the area under the graph and focused longer on the axis labels of finance graphs, indicating higher cognitive demands. In this eyetracking study, we aim for a generalization of the results obtained by Susac et al. by comparing physics students to another nonphysics sample, viz., economics students. The findings broadly confirm the results of Susac et al.; that is, physics students perform better than nonphysics students. While economics students likely have better prior knowledge on finance context than psychology students, the physics students still outperform them on the finance questions. In contrast to the work by Susac et al., both groups of students had the same visit duration on the graphs, consequently proving total dwell time to be an inadequate predictor of performance. Instead, we identify that attention on concept-specific areas of interest within the graphs discriminates the correct from the incorrect performers. Furthermore, we analyzed the confidence level of the two student groups and found that physics students have a higher ability to correctly judge their own performance compared to economics students. Overall, our results highlight the importance of an instructional adjustment towards a more mathematical-and graphical-based education.

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What Can We Learn from Theoretical Considerations and Empirical Evidence on Learning in Higher Education? Implications for an Interdisciplinary Research Framework

Zlatkin‐Troitschanskaia

Brückner

Molerov

et al. 2019

Frontiers and Advances in Positive Learning in the Age of InformaTiOn (PLATO)

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Threshold Concepts for Modeling and Assessing Higher Education Students’ Understanding and Learning in Economics

Cited by 9 publications

References 21 publications

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

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

Student understanding of graph slope and area under a curve: A replication study comparing first-year physics and economics students

What Can We Learn from Theoretical Considerations and Empirical Evidence on Learning in Higher Education? Implications for an Interdisciplinary Research Framework

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