This research aims to compare two experiential learning methods' effectiveness for (1) a deeper understanding of structural behaviour, and (2) skills to design architectural forms that are structurally informed. A course was planned to investigate the effect of the type and order of the two teaching units: (1) guided experiments on a parametric design model, and ( 2) parametric design of a tower and custom experiments using Grasshopper and Karamba. Results indicate that the group that started with the experiments learned to ask the relevant questions by experimenting with the appropriate parameters that helped them to find the structural principles and apply them during their design phase. The group that started with the design were lost in the structural concepts and in identifying the meaningful parameters to test for. However, after the experiment was completed, this group could make a knowledge transfer. Acquisition of structures knowledge may require the experience of multiple situations while the application of this knowledge may involve selecting the relevant structural experience with the architectural form-finding process. In the future, a proposed experiential learning method will be compared with an instructive learning approach of structural systems for architecture students.

Computational design has brought in novel concepts to architecture and design disciplines. Computational design thinking has evolved due to the potentials of contemporary tools and methods. Experiential learning environments such as computational design workshops offer strategies for a better understanding of the contemporary needs of the computational design education. Smartgeometry (SG) is a computational design organization that operates through workshops of interdisciplinary teams. SG uses and teaches the state-of-the-art computational design tools and methods. Instead of teaching the novel computational design tools in an instructive manner, SG workshops focus on using the potentials of these tools through personal discovery and experimentation. Besides enabling responsive design outputs, tools for sensing, computing and materializing lead to various learning strategies such as learning-by-doing, interdisciplinary collaboration and community building by democratization. This study aims to unravel the impacts of the novel computational design tools and strategies on computational design education through an in-depth qualitative analysis of the SG workshops.

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