Comparing Optimization Practices Across Engineering Learning Contexts Using Process Data

Abstract

Despite an increasing focus on integrating engineering design in K-12 settings, relatively few studies have investigated how to support students to engage in systematic processes to optimize the designs of their solutions. Emerging learning technologies such as computational models and simulations enable rapid feedback to learners about their design performance, as well as the ability to research how students may or may not be using systematic approaches to the optimization of their designs. This study explored how middle school, high school, and pre-service students optimized the design of a home for energy efficiency, size, and cost using facets of fluency, flexibility, closeness, and quality. Results demonstrated that students with successful designs tended to explore the solution space with designs that met the criteria, with relatively lower numbers of ideas and fewer tightly controlled tests. Optimization facets did not vary across different student levels, suggesting the need for more emphasis on supporting quantitative analysis and optimization facets for learners in engineering settings.

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Journal of Science Education and Technology (2024) 33:143–155
https://doi.org/10.1007/s10956-023-10080-x
Comparing Optimization Practices Across Engineering Learning
Contexts Using Process Data
JenniferL.Chiu1 · JamesP.Bywater2· TugbaKarabiyik3· AlejandraMagana3· CoreySchimpf4· YingYingSeah5
Accepted: 12 October 2023 / Published online: 31 October 2023
© The Author(s), under exclusive licence to Springer Nature B.V. 2023
Abstract
Despite an increasing focus on integrating engineering design in K-12 settings, relatively few studies have investigated how
to support students to engage in systematic processes to optimize the designs of their solutions. Emerging learning technolo-
gies such as computational models and simulations enable rapid feedback to learners about their design performance, as well
as the ability to research how students may or may not be using systematic approaches to the optimization of their designs.
This study explored how middle school, high school, and pre-service students optimized the design of a home for energy
efficiency, size, and cost using facets of fluency, flexibility, closeness, and quality. Results demonstrated that students with
successful designs tended to explore the solution space with designs that met the criteria, with relatively lower numbers of
ideas and fewer tightly controlled tests. Optimization facets did not vary across different student levels, suggesting the need
for more emphasis on supporting quantitative analysis and optimization facets for learners in engineering settings.
Keywords Optimization· Engineering design· Secondary education· Pre-service teachers· CAD simulation
Engineering in pre-college settings is gaining traction, with
many states adopting the Next Generation Science Stand-
ards (NGSS, 2013) as well as offering standalone engi-
neering curricula (e.g., Project Lead the Way, n.d.). Inte-
grating engineering into pre-college settings enables more
students to participate in the discipline, offering broader
access to those who may not know about engineering as
a field or career (e.g., Cunningham & Lachapelle, 2014;
Moore etal., 2014a).
Many efforts have characterized what should be included in
K-12 engineering. For example, the Framework for P-12 Engi-
neering Learning (Advancing Excellence in P12 Engineering
Education & American Society for Engineering Education
[ASEE], 2020) provides a three-dimensional taxonomy of
engineering habits of mind, engineering knowledge, and
engineering practice. Engineering habits of mind include
traits such as optimism, creativity, persistence, and collabo-
ration. Engineering knowledge consists of the mathematics,
science, and technical expertise required to design solutions
and complete engineering tasks. Engineering practice involves
engineering design, material processing, professionalism, and
quantitative analysis. As such, engineering design has been
promoted as a context for science and math learning (English,
2016), as well as integrated STEM learning (Moore etal.,
2014b; Purzer & Quintana-Cifuentes, 2019), in part because
these projects can help students develop skills that transcend
engineering design problems and have applicability across a
wide set of problem-solving scenarios. Although a fair amount
of research has focused on the engineering design in K-12 set-
tings (e.g., Lammi etal., 2018; Purzer etal., 2014), relatively
little has focused on the practice of quantitative analysis in
pre-college engineering settings (e.g., Chao etal., 2017). In
particular, few research studies investigate how pre-college
students use systematic processes to optimize the designs
James P. Bywater, Tugba Karabiyik, Alejandra Magana, Corey
Schimpf, and Ying Ying Seah contributed equally to this manuscript
* Jennifer L. Chiu
jlchiu@virginia.edu
1 Curriculum, Instruction, andSpecial Education, School
ofEducation andHuman Development, University
ofVirginia, 313 Bavaro, Charlottesville, VA22903, USA
2 Learning, Technology, & Leadership Education, James
Madison University, Harrisonburg, USA
3 Computer andInformation Technology, Purdue University,
WestLafayette, USA
4 Department ofEngineering Education, University atBuffalo,
Buffalo, USA
5 School ofBusiness, Oakland City University, Oakland, USA
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