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This paper provides a theoretical perspective of how modeling and simulation on a CAD platform can be used to teach science concepts and inform design decisions. The paper discusses the educational implications of three recent advancements in CAD technologies: system integration, machine learning, and computational design. The challenges to design energy‐efficient buildings that harness solar energy are used as the engineering examples to illustrate the learning and teaching opportunities created by the modeling, simulation, and data mining capabilities of the Energy3D software, which is a CAD tool developed from scratch along the directions of the three advancements to support engineering research and education. Preliminary results from students in a physics classroom and an online course shed light on the effects of these features on guiding student to design cost‐effective rooftop solar power systems for their own home buildings.

Background The participation of women in engineering education has increased only slightly since the 1980s, despite the publication of many research studies on gender in engineering education. We think that these studies have not affected practice because researchers have focused too narrowly on how gender relates to engineering education. Purpose This article investigates whether there is indeed a narrow focus by analyzing how articles published in JEE investigate gender. We asked, What are the dominant themes and patterns in the structure of gender research published in JEE? We wanted to see how engineering education research articles incorporated gender theory and research methods from the social sciences and education to explore the relationships between gender and engineering education. Design/Method We conducted a content analysis of gender‐related research published in JEE between 1998 and 2012. We developed scientometric and other classification categories and applied them quantitatively. Results Articles related to gender are predominantly quantitative studies that focus on undergraduate students in formal university settings, and incorporate participant identities in the groups of women and men (together) or women, men, and racial minorities (together). Researchers used varied theories of gender, but most of those theories were not used again in later research in the articles analyzed. Conclusions A greater diversity of theories and designs should lead to a better understanding of gender in engineering education. We suggest areas for future research.

Design thinking is often hidden and implicit, so empirical approach based on experiments and data-driven methods has been the primary way of doing such research. In support of empirical studies, design behavioral data which reflects design thinking becomes crucial, especially with the recent advances in data mining and machine learning techniques. In this paper, a research platform that supports data-driven design thinking studies is introduced based on a computer-aided design (cad) software for solar energy systems, energy3d, developed by the team. We demonstrate several key features of energy3d including a fine-grained design process logger, embedded design experiment and tutorials, and interactive cad interfaces and dashboard. These features make energy3d a capable testbed for a variety of research related to engineering design thinking and design theory, such as search strategies, design decision-making, artificial intelligent (AI) in design, and design cognition. Using a case study on an 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 design action data and unsupervised clustering methods. The results validate the utility of energy3d as a research platform and testbed in supporting future design thinking studies and provide domain-specific insights into new ways of integrating clustering methods and design process models (e.g., the function–behavior–structure model) for automatically clustering sequential design behaviors.

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is a Learning Analytics Scientist at the non-for-profit Concord Consortium, which develops technology and curriculum for STEM learning in K-12. One avenue of his work focuses on the development and analysis of learning analytics that model students' cognitive states or strategies from fine-grained computer-logged data from students participating in open-ended technology-centered science and engineering projects. In another avenue of his work he develops assistive software to help researchers dealing with complex, high dimensional problems, such as an integrated sets of methodological tools or a multi-purpose data processing tools for high volume data with limited structure. His dissertation research explored the use of Minecraft to teach early engineering college students about the design process.Dr. Charles Xie, The Concord Consortium

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Two leading camps for studying social complexity are case-based methods (CBM) and agent-based modelling (ABM). Despite the potential epistemological links between 'cases' and 'agents,' neither camp has leveraged their combined strengths. A bridge can be built, however, by drawing on Abbott's (1992) insight that "agents are cases doing things", Byrne's (2009) suggestion that "cases are complex systems with agency", and by viewing CBM and ABM within the broader trend towards computational modelling of cases. To demonstrate the utility of this bridge, we describe how CBM can utilise ABM to identify casebased trends; explore the interactions and collective behaviour of cases; and study different scenarios. We also describe how ABM can utilise CBM to identify agent types; construct agent behaviour rules; and link these to outcomes to calibrate and validate model results. To further demonstrate the bridge, we review a public health study that made initial steps in combining CBM and ABM.