Engineers typically approach design in teams, particularly when dealing with complex problems that may need to be decomposed into several parts or subsystems to be designed individually and integrated. Team design projects during students’ college years can serve as critical experiences to prepare for professional work on design teams. However, the volume of actions across team members and iterative nature of engineering design makes tracking, representing and learning from design teams’ actions difficult and time-consuming. This work proposes developing design team analytics as a tool for representing and understanding how students collectively navigate and address complex designs, by leveraging a computer-aided design (CAD) platform with action-logging functionality.
A class of 28 juniors and seniors in a project-based engineering program at a small Midwestern University worked in teams of four-to-five to design a distributed system of solar arrays for their local community while balancing energy need and budgetary constraints. Students were given a suite of 8 solarizable sites including flattop, pitched-roof buildings, and parking lot locations. Students then used these sites to design, evaluate, and select a subset for their final design. Energy3D, a CAD platform for constructing buildings and solar arrays that features many analytical tools, served as the primary design platform. Importantly, Energy3D logs users’ actions such as adding a solar panel or running an annual solar yield. The data from these logs was examined in terms of individual and collective contributions resulting in visualizations of the teams’ design processes across several metrics including: construction, optimization, and numerical analysis.
Preliminary results for this work-in-progress indicate that students mostly designed sequentially across solarizable sites, with little concurrent activity. Optimization patterns vary between teams and show some relation to teams’ final design(s) performance.
Corey Schimpf is a Learning Analytics Scientist with interest in design research, learning analytics, research methods and under-representation in engineering, A major strand of his work focuses on developing and analyzing learning analytics that model students’ cognitive states or strategies through fine-grained computer-logged data from open-ended technology-centered science and engineering projects. His dissertation research explored the use of Minecraft to teach early engineering college students about the design process.
http://orcid.org/0000-0003-0753-2707
Minnesota State University, Mankato
[biography]
Rob Sleezer earned his Ph.D. in Microelectronics-Photonics from the University of Arkansas. He attended Oklahoma State University where he graduated with a B.S. in Computer Science and an M.S. and B.S. in Electrical Engineering. He is currently a faculty in the Twin Cities Engineering program of Minnesota State University, Mankato.
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