Abstract
The goal of this study is to advance the teaching and learning of uncertainty in conceptual design. The central research question is: Can a data-driven conceptual design course improve students’ ability to reason about aleatory and epistemic uncertainty? To investigate this, two aims were pursued: (1) constructing a data-driven conceptual design course with implementation and evaluation strategies, and (2) designing an educational flow that supports students’ engagement with uncertainty through structured tasks. Nine frameworks, grouped into five categories and supported by three discipline-based education research fields, were defined to ground the study and provide a foundation for addressing the research question. Using the backward design, a comprehensive conceptual design course was proposed, aligned with relevant ABET competencies and complemented by an educational flow and an educator's guide containing theoretical preparation materials, implementation tools, recommended programming libraries, and guidance for undergraduate and graduate-level instruction. A case study, based on bicycle frame design, demonstrated practical implementation through image preprocessing, dimensionality reduction, and clustering. The course was further contextualized to illustrate the applicability across multiple STEM fields, including mechanical, electrical/computer, and biomedical engineering. Overall, this study contributes a generalizable teaching-learning-assessment construct for supporting uncertainty reasoning in advanced engineering design courses.
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