Affordances and challenges of computational tools for supporting modeling and simulation practices

Magana, Alejandra J.; Falk, Michael L.; Vieira, Camilo; Reese, Michael J.; Alabi, Oluwatosin O.; Patinet, Sylvain

doi:10.1002/cae.21804

Cited by 23 publications

(15 citation statements)

References 59 publications

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“…However, research has identified obstacles that undergraduate students often encounter when engaging in modeling and simulation practices. The obstacles identified have primarily been related to students' inability to map the relationships among the physical phenomena, the mathematical representation, and the computational representation (Magana et al, , ). However, when thoughtfully exposed to modeling and simulation practices more intensively (e.g., for a semester‐long term), undergraduate students have demonstrated their ability to acquire foundational computing concepts and procedures for solving well‐structured engineering problems (Magana, Falk, & Reese, ; Magana, Falk, Vieira, & Reese, ).…”

Section: Introductionmentioning

confidence: 99%

“…Within a relative approach to study modeling and simulation in engineering education, a case study conducted with novice materials engineering undergraduates characterized challenges they encountered when engaged with modeling and simulation practices. As part of a think‐aloud procedure, Magana et al () identified that in some instances students struggled with the complexity of the graphical user interface. In other instances, students encountered significant challenges with making connections among different forms of representation across physical phenomena and conceptual, mathematical, and computational representations (Magana et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…As part of a think‐aloud procedure, Magana et al () identified that in some instances students struggled with the complexity of the graphical user interface. In other instances, students encountered significant challenges with making connections among different forms of representation across physical phenomena and conceptual, mathematical, and computational representations (Magana et al, ). Similarly, students often made representational connections that were misleading, fragmented, or fallacious (Magana et al, ).…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Magana

Fennell

Vieira

et al. 2019

J of Engineering Edu

Self Cite

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“…This finding was already expected by the research team, since preliminary work has identified that students face difficulties when learning programming, such as struggles with getting familiar with programming structure and tools, designing applied solutions, and fixing bugs 21 . Preliminary work has also identified that engaging students in a programming approach to modeling and simulation may pose programming challenges 6 . We believe, however, that when students engage in a programming approach they follow a constructive approach to learning, and when students follow a configuring approach they engage in an active approach to learning.…”

Section: Discussionmentioning

confidence: 99%

“…Computational tools are sometimes employed to help teach or illustrate the Mohr's circle technique through computer simulation, but these simulations often use a "configuring approach" to computational thinking, in which students alter input parameters of the system and the program outputs the resulting diagram 4,5 . This study presents a method for simultaneously teaching Mohr's circle diagram concepts and computational literacy through a "programming approach" in which students are asked to construct, operate, and interpret results from a computational simulation 6 . The research question is: What are students' benefits and challenges when making meaning of Mohr's Circle diagrams following a "programming approach?…”

Section: Introductionmentioning

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

Magana

Coutinho

2016

Comp Applic In Engineering

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Computational thinking has been recognized as a collection of understandings and skills required for new generations of students not only proficient at using tools, but also at creating them and understanding the implication of their capabilities and limitations. This study proposes the combination of modeling and simulation practices along with disciplinary learning as a way to synergistically integrate and take advantage of computational thinking in engineering education. This paper first proposes a framework that identifies different audiences of computing and related computational thinking practices at the intersection of computer science and engineering. Then, based on a survey with 37 experts from industry and academia, this paper also suggests a series of modeling and simulation practices, methods, and tools for such audiences. Finally, this paper also reports experts' identified challenges and opportunities for integrating modeling and simulation practices at the undergraduate level. ß 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:62-78, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21779

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Affordances and challenges of computational tools for supporting modeling and simulation practices

Cited by 23 publications

References 59 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

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

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

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