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A Qualitative Study of Engineering Students' Reasoning About Statistical Variability

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Conference

2021 Fall ASEE Middle Atlantic Section Meeting

Location

Virtually Hosted by the section

Publication Date

November 12, 2021

Start Date

November 12, 2021

End Date

November 13, 2021

Page Count

17

Permanent URL

https://peer.asee.org/38421

Download Count

17

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Paper Authors

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Riya Aggarwal Franklin W. Olin College of Engineering

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Riya is senior at the Olin College of Engineering studying Engineering with a concentration in User Experience Design.

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Mira Flynn Olin College of Engineering

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Mira is a 3rd year undergraduate student at Olin College. They are majoring in Engineering with a concentration in Computing.

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Sam Daitzman Olin College of Engineering

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Sam Daitzman is a senior studying Engineering with a concentration in Human-Centered Product Design/Computing at Olin College of Engineering.

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Diane Lam

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Zachary Riggins del Rosario Franklin W. Olin College of Engineering Orcid 16x16 orcid.org/0000-0003-4676-1692

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Zachary del Rosario is a visiting assistant professor at Olin College. His goal is to help scientists and engineers reason under uncertainty. Zach uses a toolkit from data science and uncertainty quantification to address a diverse set of problems, including reliable aircraft design and AI-assisted discovery of novel materials.

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Abstract

Every aircraft you have ever flown on has been designed using probabilistically-flawed, potentially dangerous criteria (del Rosario et al. 2021, AIAA-J). That these criteria have been in use for over a half-century---but were only recently identified as dangerous---speaks to the difficulty of teaching such concepts to engineering students. Clearly, improvements to teaching probability and statistics to engineers are needed. Key to statistical thinking is an understanding of and facility with variability (Wild and Pfannkuch, 1999).

This work lays foundations for improved statistical literacy among engineers, enabling a multi-disciplinary approach to engineering statistics. We present a novel theoretical framework for both teaching statistical variability and studying engineers' reasoning under variability. While past frameworks for variability focus on a statistician's perspective (e.g. Peters 2011), our framework centralizes the engineering perspective by explicitly considering physical modeling, and makes use of established insights in manufacturing variability (Shewhart 1931).

Using this framework, we have developed an interview protocol and deductive coding scheme, and deployed these qualitative tools in interviews with engineering students. Results from these interviews support the validity of the coding scheme, agree with documented trends in current aerospace practice, and suggest possible teaching interventions for innovative engineering pedagogy. Implications for future multi-disciplinary teaching of engineering statistics will be discussed.

del Rosario, Fenrich, and Iaccarino, "When are Allowables Conservative?" (2021) AIAA Journal

Wild and Pfannkuch, "Statistical Thinking in Empirical Enquiry" (1999) International Statistical Review

Peters "Robust Understanding of Statistical Variation" (2011) Statistics Education Research Journal

Shewhart, Economic control of quality of manufactured product (1931) Macmillan And Co. Ltd, London

Aggarwal, R., & Flynn, M., & Daitzman, S., & Lam, D., & del Rosario, Z. R. (2021, November), A Qualitative Study of Engineering Students' Reasoning About Statistical Variability Paper presented at 2021 Fall ASEE Middle Atlantic Section Meeting, Virtually Hosted by the section. https://peer.asee.org/38421

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