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Comparing Optimization Practices Across Engineering Learning Contexts Using Process Data

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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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Funding

This material is based upon work supported by the National Science Foundation under Grant Nos. DRL-1503170 and 1503436.

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Authors and Affiliations

  1. Curriculum, Instruction, and Special Education, School of Education and Human Development, University of Virginia, 313 Bavaro, Charlottesville, VA, 22903, USA

    Jennifer L. Chiu

  2. Learning, Technology, & Leadership Education, James Madison University, Harrisonburg, USA

    James P. Bywater

  3. Computer and Information Technology, Purdue University, West Lafayette, USA

    Tugba Karabiyik & Alejandra Magana

  4. Department of Engineering Education, University at Buffalo, Buffalo, USA

    Corey Schimpf

  5. School of Business, Oakland City University, Oakland, USA

    Ying Ying Seah

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  1. Jennifer L. Chiu
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Correspondence to Jennifer L. Chiu.

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Ethics Approval

All procedures involving human participants in this study were in accordance with the ethical standards of the Institutional Review Boards of the universities. This study was approved by the Human Research Protection Program/Review Board for Social and Behavioral Sciences at the two universities.

Consent Statement

Informed consent was obtained from all adult participants included in the study. The middle school and high school contexts were deemed as normal educational practice in educational settings by the Institutional Review Board. Informed notification of the study was given to parents/guardians of middle and high school students including study purposes, data gathered, processes to make the data confidential and secure, as well as contact information to withdraw from the study.

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James P. Bywater, Tugba Karabiyik, Alejandra Magana, Corey Schimpf, and Ying Ying Seah contributed equally to this manuscript

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Chiu, J.L., Bywater, J.P., Karabiyik, T. et al. Comparing Optimization Practices Across Engineering Learning Contexts Using Process Data. J Sci Educ Technol 33, 143–155 (2024). https://doi.org/10.1007/s10956-023-10080-x

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