The Complementary Nature of Computational Thinking and Critical Making in a Project-based Learning (PjBL) course on Design for Manufacture (DfM) - Ciriello, Francesco; Awotwe, Tabbi Wilberforce; Gong, Jicheng; Piper, Samuel; Palmer, Charlotte; Denniss, Ana Rubio (Proceedings from the International Research Symposium on Problem-Based Learning (IRSPBL), 2025)
In Engineering Project-based Learning (PjBL), learners often develop a physical prototype of a product, which provides an authentic context for the learning and experiential feedback on the design process. We often struggle as educators, however, to support learners in the analysis of the feasibility, viability and sustainability of the product when brought to production. This may be caused by the everchanging nature of supply chains and consumer demand, high capital costs associated to high production volumes, and the technical skills shortage within research-focused universities when considering how to scale commercial products for wide consumption. In developing a new PjBL course at King’s College London, we study how learners develop competencies for integrated problem solving associated to design for manufacture (DfM). Acknowledging the steep learning curve required to support open-ended projects, we have intentionally pushed the boundaries of how the design process is supported by computational thinking techniques, including automated quoting engines and embodied carbon estimators, generative design techniques, geometric design advice, AI-assisted digital product passport generation, manufacturing simulation and automated operation and toolpath planning. We study how these teaching innovations compound to shape the ability of a student to critically change the geometry of a component in response to multi-variate performance metrics. To this end, we compare the geometric features of more than 60 CAD models submitted by learners in the second week of the course, compared to the final product submitted after 12 weeks at the end of the course. We link this analysis to a thematic analysis of short reports that highlight key trade-offs for cost, sustainability and other performance metrics. This methodology allows us to evaluate our cohort’s decision-making process in selecting projects, computational techniques, and manufacturing strategies. The analysis reveals a strong yet nuanced relationship between the adoption and application of computational redesign tools and the quality and manufacturability of product designs. It also allows us to categorise various cognitive responses to the application of computational techniques in relation to DfM and relate these to outcomes.