Modeling and simulation practices for a computational thinking‐enabled engineering workforce

Magana, Alejandra J.; Coutinho, Genisson Silva

doi:10.1002/cae.21779

Cited by 72 publications

(71 citation statements)

References 39 publications

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“…Specifically, employers from various engineering sectors value students' abilities to “understand engineering principles and computational principles that allow them to use computational tools to solve engineering problems by moving between physical systems and abstractions in software” (Vergara et al, , p. 6). Similarly, engineering education policymakers and the engineering education research community have recommended the incorporation of modeling and simulation skills into the undergraduate engineering curriculum (Magana, ; Magana & Coutinho, ). For instance, the Transforming Undergraduate Education in Engineering Report (American Society for Engineering Education, ), recently identified that industry professionals value programming skills and the ability to use computational tools to support problem‐solving.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the interplay of cognitive and metacognitive knowledge in computational modeling and simulation practices

Magana

Fennell

Vieira

et al. 2019

J of Engineering Edu

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Background Modeling and simulation practices have become commonplace in modern engineering, but in‐depth research on the development of modeling and simulation expertise is needed to identify the learning processes involved in successfully acquiring these skills. Purpose This study investigated student dimensions of expertise when engaged in modeling and simulation practices. The guiding research question was “How do students use cognitive and metacognitive knowledge when engaged with computational modeling and simulation practices?” Design/Method This study included 11 undergraduate engineering students enrolled in a course on computational materials science. Data were collected from one of five computational challenges administered during the course. Students' cognitive and metacognitive knowledge use was first analyzed using thematic analysis and then through a phenomenographic approach. Findings were interpreted using the lens of adaptive expertise. Results Results describe how students used their cognitive and metacognitive knowledge to approach the computational challenge. Two categories in the cognitive dimension were identified: implementation‐oriented and knowledge‐oriented. Two categories identified in the metacognitive dimension were plan‐oriented and action‐oriented approaches. These categories are further presented as an outcome space by aligning them with the dimensions of adaptive expertise. Conclusions Results indicated that students in the action/implementation‐oriented category exhibited many of the qualities expected of inexperienced novices, while students in the plan/knowledge‐oriented category demonstrated more of the qualities expected of adaptive experts. However, results were less conclusive for students in the two intermediate thematic categories, who often exhibited some but not all of the qualities of both novices and experts.

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Section: Introductionmentioning

confidence: 99%

Characterizing the interplay of cognitive and metacognitive knowledge in computational modeling and simulation practices

Magana

Fennell

Vieira

et al. 2019

J of Engineering Edu

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“…Besides conventional techniques, it is very important to teach engineering students about computer‐based solutions and modeling skills for making them to develop more precise and practical solutions to the problems that they will encounter in their professional lives. Additionally, presentation of course‐related experiments to curriculum provides an important opportunity for engineering students in comprehension of the theoretical topics in a visual way.…”

Section: Introductionmentioning

confidence: 99%

“…There are numerous commercially available software packages for different research areas in engineering applications , and these can be classified as spreadsheet‐based, equation solver‐based, and dynamic simulation‐based packages . It can be noted that various versatile engineering software and programming languages, such as MATLAB (MathWorks Inc., Inc., Natick, MA), Excel (Microsoft Inc., Redmond, WA), AutoCAD (Autodesk Inc., San Rafael, CA), Simulink (MathWorks Inc.), DataFit (Oakdale Eng., Oakdale, PA), Python (Python Softw.…”

Section: Introductionmentioning

confidence: 99%

IMECE—Implementation of mathematical, experimental, and computer‐based education: A special application of fluid mechanics for civil and environmental engineering students

Yetilmezsoy

2017

Comp Applic In Engineering

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A novel educational methodology, Implementation of Mathematical, Experimental, and Computer‐based Education (IMECE), where its name was derived from the letters of the word “imece” (a Turkish tradition) by taking into account the integrative meaning of this cultural concept, was proposed to explore the synergistic effect of the conventional mathematical solution procedure, visual application, and MATLAB‐based simulation on the perceptions and learning quality of the engineering students. Within the scope the present research carried out both at the state university (undergraduate and graduate levels) and at the foundation university (undergraduate level), water discharge problem (a special case of the Bernoulli's principle/Toricelli's law) was chosen as an example application. In the study, the experimental results of three different tank models (vertical cylindrical, conical, and spherical) and the solution algorithms written in MATLAB were presented in detail, and new empirical equations were also developed for each laboratory scale system depending on the experimental data. The effectiveness of the proposed IMECE method was evaluated statistically within the framework of both “Student Survey” and “Student Assessment” conducted with the participation of 84 students. Based on the mean scores obtained for all participating students, the results of the statistical tests indicated that the quantitative performance (or effectiveness) of the applied IMECE methodology was determined as 97.73%, 67.36%, and 82.55% for the perception/satisfaction section, the learning section, and the overall evaluation category, respectively.

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“…This approach resonates with the ideas of Perkins, who argued that to make learning more meaningful to students, educators must "play the whole game" by letting students work on applied problems linked with professional situations 24 . Second, it allows students to integrate modeling with the computational tools 28 . Aside from increasing students' motivation, the integration of modeling with computational tools promotes the development of professional skills highly recognized by industry 28 .…”

Section: "The Step That Helped Me the Most In My Learning Would Actuamentioning

confidence: 99%

“…Second, it allows students to integrate modeling with the computational tools 28 . Aside from increasing students' motivation, the integration of modeling with computational tools promotes the development of professional skills highly recognized by industry 28 . Third, this approach seems to foster the development of higher levels of conceptual understanding, as indicated by students' perceptions of understanding stress transformation, and of understanding stress/strain relationships, the two core concepts embedded in the Mohr's circle study.…”

Section: "The Step That Helped Me the Most In My Learning Would Actuamentioning

confidence: 99%

Enhancing Student Meaning-Making of Threshold Concepts via Computation: The Case of Mohr’s Circle

Fennell¹,

Coutinho²,

Magana³

et al.

2017 ASEE Annual Conference &Amp; Exposition Proceedings

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He is a mechanical engineer and holds a Bachelor's degree in law and a Master's degree in mechanical engineering. He has been teaching at different levels, from the first year of technical high school to the final year of mechatronic engineering course, since 1995. He also has considerable experience in the design and implementation of mechatronic and production engineering courses. His non-academic career is centered on product development and manufacturing processes. Center as a staff researcher, where he led research activities in the general areas of computational mechanics, smart and biomimetic materials. His current research lies at the interface between mechanics and materials engineering. His engineering and scientific curiosity has focused on the fundamental aspects of how Nature uses elegant and efficient ways to make remarkable and more sustainable materials. He has contributed to the area of biomimetic materials by investigating the structure-function relationship of naturally-occurring high-performance materials at multiple length-scales, combining state-of-the-art computational techniques and experiments to characterize the properties.

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Modeling and simulation practices for a computational thinking‐enabled engineering workforce

Cited by 72 publications

References 39 publications

Characterizing the interplay of cognitive and metacognitive knowledge in computational modeling and simulation practices

Characterizing the interplay of cognitive and metacognitive knowledge in computational modeling and simulation practices

IMECE—Implementation of mathematical, experimental, and computer‐based education: A special application of fluid mechanics for civil and environmental engineering students

Enhancing Student Meaning-Making of Threshold Concepts via Computation: The Case of Mohr’s Circle

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