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.
The cellular translocon, present in all three domains of life, is one of the most versatile and important biological nanopores. This complex molecular apparatus is directly responsible for the secretion of globular proteins across membranes as well as the insertion of integral membrane proteins into lipid bilayers. Recently determined structures of the archaean SecY translocon reveal an hour-glass-shaped pore, which accommodates the nascent peptide chain during translocation. While these structures provide important insights into ribosome binding to the translocon, threading of the nascent chain into the channel, and lateral gate opening for releasing the folded helical peptide into the membrane bilayer, the exact folding pathway of the peptide inside the protein-conducting channel during translocation and prior to the lateral release into the bilayer remains elusive. In the present study, we use molecular dynamics simulations to investigate atomic resolution peptide folding in hour-glass-shaped pore models that are based on the SecY translocon channel structure. The theoretical setup allows systematic variation of key determinants of folding, in particular the degree of confinement of the peptide and the hydration level of the pore. A 27-residue hydrophobic peptide was studied that is preferentially inserted into membranes by the translocon. Our results show that both pore diameter as well as channel hydration are important determinants for folding efficiency and helical stability of the peptide, therefore providing important insights into translocon gating and lateral peptide partitioning.
The conceptualization of Computational Thinking as a cross-cutting skill with relevance across disciplines has ushered in wide-ranging efforts to increase computational education in all facets of education. However, the majority of initiatives for integrated computing education have focused on K-12 settings, as has most education research around computational thinking. At the postsecondary level, computing education remains largely siloed within specific programming courses and has not been well-integrated throughout the STEM curriculum. Current instructional approaches often leave students poorly prepared to transfer their computing knowledge to solve new real-world problems. Additionally, there is limited education research into how best to develop computational thinking among postsecondary students. In fact, education research into computational thinking remains undertheorized and is often definitional in nature. Here, we integrate computational thinking with the educational psychology concept of adaptive expertise. Finally, we contextualize computational thinking within constructivist learning theories by introducing computational apprenticeship, an application of cognitive apprenticeship to computing. Computational apprenticeship provides a research and practice model for supporting the development of computational adaptive expertise.
Computational modeling is an essential and growing topic of interest in engineering and STEM education in general. In this paper, a case study of a course targeting first-year materials science and engineering students is overviewed to investigate student arguments when explaining their implementation and understanding of their computational models. The presented study used argumentation to characterize the varied ways students interpreted and explained their computational models. Student solutions to a computational modeling activity were coded for argumentation characteristics and errors. The results indicate the various errors that were commonly found when students explained their computational models, as well as patterns of appropriate arguments. The analysis demonstrates the nature of the relationship between argumentation and the modeling processes. The use of an argumentation rubric demonstrates the usefulness of such assessment tools for computational modeling assignments.
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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