Design thinking is often hidden and implicit, so empirical and data-driven approaches have been the primary ways to studying it. In support of empirical studies, design behavioral data which reflects design thinking is crucial to foundational research, especially with the recent advancement in data mining and machine learning techniques. In this paper, a research platform that supports data-driven engineering design thinking studies is introduced based on a computer-aided design (CAD) software for solar energy systems, Energy3D, developed by the team. We demonstrated several key features of Energy3D including fine-grained sequential design action logger, embedded design experiment and tutorials, and interactive CAD interfaces and dashboard. These features make Energy3D a capable platform for a variety of research related to engineering design thinking and design theory, such as search strategies, design decision-making, AI in design, and design cognition. Using a case study on the energy-plus home design challenge, we demonstrate how such a platform enables a complete research cycle of studying designers’ sequential decision-making behaviors based on fine-grained sequential design action data and unsupervised clustering techniques. The results validate the feasibility and utility of Energy3D as a research platform in data-driven design thinking studies and provide domain-specific insights into new ways of integrating clustering methods and design process models for automatically clustering sequential design behaviors.

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