Fishers Handle Bugs Better than Fish-Receivers: Nourishing Computational Self-Efficacy in Engineering Coursework
- Conference
- Location
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Minneapolis, MN
- Publication Date
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August 23, 2022
- Start Date
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June 26, 2022
- End Date
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June 29, 2022
- Conference Session
- Page Count
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8
- DOI
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10.18260/1-2--41638
- Permanent URL
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https://peer.asee.org/41638
- Download Count
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215
Paper Authors
Seyed-Arman Ghaffari-Zadeh Carnegie Mellon University
Arman Ghaffarizadeh is a Ph.D. student in Mechanical Engineering at Carnegie Mellon University. He obtained his Bachelor’s degree (high honours) and Master’s degree in Mechanical Engineering, from the University of Toronto in 2016 and 2018, respectively. He is a recipient of numerous NSERC awards for his undergraduate research (USRA), master’s work (CGS M), and PhD (CGS D/PGS D) studies. He was selected as a Russell A. Reynolds Graduate Fellow in thermodynamics and was awarded the Ontario Graduate Scholarship (OGS) for his work on multiphase systems. Arman is involved in numerous student committees and advocacy roles and was the recipient of the Society of Petroleum Engineers (SPE) and Jeremieh Mpagazehe Awards for his service and contributions to the graduate student community. In addition to his research focus on nanoscale transport phenomena, he conducted and published an international study on the effect of the COVID-19 pandemic on the life and work of academic researchers.
Gerald Wang Carnegie Mellon University
Jerry Wang is an Assistant Professor of Civil and Environmental Engineering, and Mechanical Engineering (by courtesy) and Chemical Engineering (by courtesy), at Carnegie Mellon University. He received his BS in 2013 from Yale University (Mechanical Engineering, Mathematics and Physics), SM in 2015 from MIT (Mechanical Engineering), and PhD in 2019 from MIT (Mechanical Engineering and Computation). He performed postdoctoral research at MIT in Chemical Engineering. He was a member of the inaugural cohort of the Provost’s Inclusive Teaching Fellowship at CMU, was the 2020 recipient of the Frederick A. Howes Scholar Award in Computational Science and the 2016 MIT Graduate Teaching Award in the School of Engineering, and is an alumnus of the Department of Energy Computational Science Graduate Fellowship and the Tau Beta Pi Graduate Fellowship.
Wang directs the Mechanics of Materials via Molecular and Multiscale Methods Laboratory (M5 Lab) at CMU, which focuses on computational micro- and nanoscale mechanics of fluids, soft matter, and active matter, with applications in Civil and Environmental Engineering across the nexus of water, energy, sustainable materials, and urban livability. The M5 Lab is particularly interested in particle-based simulations, systems out of equilibrium, uncertainty quantification in particle-based simulations, and high-performance computing. He teaches courses in molecular simulation and computational/data science.
Abstract
As the use of computational tools and advanced computing principles proliferates in engineering practice, a growing number of engineering curricula place heavy emphasis on developing computational reasoning skills. This pedagogical imperative presents unique challenges in graduate-level computational engineering courses, which often feature enormous heterogeneities in undergraduate background, disciplinary training and interest, and — in particular — prior exposure to computer programming and associated best practices. In this work, we focus on a graduate-level course that features a series of progressively scaffolded assignments in which students develop an elaborate molecular simulation code. We present strategies that have been deployed in this course, aimed at encouraging the development of computational self-efficacy. We also provide qualitative and quantitative assessments of these strategies, with special attention dedicated to assessment techniques that foster a growth mindset in the context of improving computational efficiency. We explore how providing students with a chance of resubmitting assignments improves learning outcomes. The positive effects are especially pronounced for students who have a smaller number of prior computing-related courses. We also discuss trends observed as a function of students’ preferred programming language, and correlations between preferred language (especially whether the language is compiled or interpreted) and likelihood of prioritizing computational efficiency. These results have natural implications for the inclusive and equitable growth of graduate-level computational engineering curricula, including and especially for graduate-level onboarding. Taken as a whole, our work highlights opportunities to encourage our students to — as the adage goes — seek substance beyond “proffered fish,” learn how to “handle bugs,” and eventually “fish for themselves.”
Ghaffari-Zadeh, S., & Wang, G. (2022, August), Fishers Handle Bugs Better than Fish-Receivers: Nourishing Computational Self-Efficacy in Engineering Coursework Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41638
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