A Literature Review Comparing Experts’ and Non-Experts’ Visual Processing of Graphs during Problem-Solving and Learning

Ruf, Verena; Horrer, Anna; Berndt, Markus; Hofer, Sarah; Fischer, Frank; Fischer, Martin R.; Zottmann, Jan; Kühn, Jochen; Küchemann, Stefan

doi:10.3390/educsci13020216

Cited by 7 publications

(1 citation statement)

References 53 publications

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“…Visual representations such as graph illustrations and diagrams increased favorability with students [17]. The graph plays a pivotal role in various domains, and it is relevant for understanding and comprehending [18]. Research from Stanford University said that visual representations are not only for lower-level work but also for more advanced or abstract [19].…”

Section: Introductionmentioning

confidence: 99%

Bond-Graph-Based Approach to Teach PID and Sliding Mode Control in Mechatronics

Guo,

Korondi,

Szemes

2023

Machines

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The main contribution of this article is creating synergy between subjects; this means that students use the same graphical tool in several subjects. So far, the bond graph has not been used in control theory, but it is the “native language” of mechatronics engineers, so we would like to introduce it into the teaching of control theory. The bond graph method is proposed as a novel teaching method to teach mechatronics subjects in the paper. The bond graph is a graphical alternative to ordinary differential equations from a mathematical standpoint. Traditionally, control theory employs ordinary differential equations, as they are familiar to control theorists. However, mathematically, both approaches are equivalent but require a slightly different approach in their application. This article highlights the mathematical similarities between the two approaches while emphasizing the distinctions in graphical representation. Another contribution is that the PID and sliding mode controller are represented using the bond graph method. In the meantime, through the use of practical examples, we effectively illustrate how the same problem can be solved using either approach. In the training materials, the PID controller and an adaptive robust sliding mode controller (ARSMC) with the bond graph are utilized as examples to demonstrate synergy in mechatronics. Finally, we present proof that mechatronic engineers achieve superior outcomes when utilizing the bond graph approach, based on test results from undergraduate students.

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Section: Introductionmentioning

confidence: 99%

Bond-Graph-Based Approach to Teach PID and Sliding Mode Control in Mechatronics

Guo,

Korondi,

Szemes

2023

Machines

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How to visualize electricity consumption anomalies: The impact of chart types on triggered emotions and eye movements

Kremsner,

Pfeiffer,

Weidinger

et al. 2023

e-Prime - Advances in Electrical Engineering, Electronics and E

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Comparison and AI-based prediction of graph comprehension skills based on the visual strategies of first-year physics and medicine students

Ruf,

Dinc,

Küchemann

et al. 2024

Phys. Rev. Phys. Educ. Res.

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Graphical representations of data are common in many disciplines. Previous research has found that physics students appear to have better graph comprehension skills than students from social science disciplines, regardless of the task context. However, the graph comprehension skills of physics students have not yet been compared with (veterinary) medicine students, both of which are disciplines that require multiple science, technology, engineering, and mathematics (STEM) courses. This study extends previous research on this subject by exploring whether physics majors possess superior graph comprehension skills due to their study discipline. Here, participants solved 24 graph comprehension tasks across various subjects, including mathematics, physics, and medicine; these tests were conducted at the beginning and end of their first semester. Graph comprehension gain was calculated based on the percentage of correct and incorrect answers in the pretest and the post-test. In addition to these comparisons, we replicated previous research that successfully distinguished correct and incorrect solvers based on their visual behavior by using a novel machine-learning method tailored to small datasets. Through this replication of statistical analyses, we aim to ensure the reliability of adaptive learning systems in the future, regardless of data size, using the same machine-learning method. Physics and medical students were found to exhibit relatively similar graph comprehension gain; this is in contrast to previous research comparing physics and non-STEM students. Our results also revealed that both physics and medical students use similar visual strategies to solve these tasks. However, correct and incorrect solvers could be distinguished via machine-learning methods regardless of their discipline. Our research suggests that visual behavior is a good predictor of graph comprehension skills. Published by the American Physical Society 2024

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A Literature Review Comparing Experts’ and Non-Experts’ Visual Processing of Graphs during Problem-Solving and Learning

Cited by 7 publications

References 53 publications

Bond-Graph-Based Approach to Teach PID and Sliding Mode Control in Mechatronics

Bond-Graph-Based Approach to Teach PID and Sliding Mode Control in Mechatronics

How to visualize electricity consumption anomalies: The impact of chart types on triggered emotions and eye movements

Comparison and AI-based prediction of graph comprehension skills based on the visual strategies of first-year physics and medicine students

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