ABSTRACT

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2)

Engineers are responsible for delivering safe, efficient solutions. For instance, automobile manufacturers need to design cars that are light enough to be gas-efficient, but still sturdy enough to protect the passenger. A complication in this design process is variability: No engineer can predict with 100% confidence what a driver will do with their car or what conditions it will encounter. Traditionally, engineers handle variability by "overdesign"---making things heavier than they need to be. However, scientists from other disciplines (such as statisticians) have more efficient ways to handle variability. A better understanding of variability---and how engineers themselves react to it---will lead to safer, more efficient engineering designs. Achieving these efficiency gains is critical for American economic competitiveness and for addressing anthropogenic climate change. Funded by the NSF's Research in the Formation of Engineers initiative, this project will study how real engineers react to variability and will train them to handle it more efficiently.

Variability is a key challenge in data analysis: In order to realize the NSF?s Big Idea of Harnessing the Data Revolution, engineers will need to have a variability-capable mindset. However, present engineering education results in professionals who struggle to recognize and manage variability in engineering applications. This lack of engineering workforce capability leads to inefficient designs, and in some cases, dangerously unreliable systems. The aim of this research is to study and improve the formation of engineers? variability-capability. The challenges of variability are well-documented: People generally have difficulty reasoning about variability in climate science, election forecasting, and matters of human judgment. This lack of variability-capability leads to climate inaction, political disengagement, and an unacceptably capricious application of justice. Focused on engineering, the primary investigator?s previous work identified decades-standing safety issues in aircraft design, stemming from a misidentification of a source of deviation (design-relevant variability) as noise (induced measurement variability). This project will be a mixed-methods study of practicing engineers: to investigate their ability to identify and treat different sources of variability, to develop a quantitative instrument to characterize present engineering workforce capabilities, and to design and deploy teaching interventions to improve engineers? variability-capability. Data collection will be paired with professional development workshops, which will synergistically create broader impacts via direct training. The project will advance our collective understanding and treatment of statistical variation, grounded in engineering practice. The mixed-methods study will fill gaps in the literature on how practicing engineers reason about variability. Working closely with engineers in professional development workshops will surface a library of real-world examples of noise and deviation, strengthening the novel theoretical framework with a diversity of practical examples. Sampling practitioners from across disciplines will also provide novel understanding of the differences across engineering fields, comparing current statistical practices in engineering and attributing them to differences in training and paradigm. The proposed work will develop and deploy teaching interventions to train data- and variability-capable engineers through professional development workshops. Thus broader impact will be realized both directly (through the professional development workshops) and indirectly (through dissemination of the teaching interventions to other institutions).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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