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Optimizing Capstone Team Selection

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Design in Engineering Education Division: Design Teams

Tagged Division

Design in Engineering Education

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Paper Authors


B. Matthew Michaelis Eastern Washington University

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Matthew Michaelis is an Assistant Professor of Mechanical Engineering and Mechanical Engineering Technology at Eastern Washington University in Cheney, WA. His research interests include additive manufacturing, advanced CAD modeling, and engineering pedagogy. Before transitioning to academia, he worked for years as a design engineer, engineering director, and research scientist and holds MS and PhD degrees from University of CA, Irvine and a B.S. degree from Walla Walla University.

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Heechang (alex) Bae Eastern Washington University

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Assistant Professor
Mechanical Engineering/Mechanical Engineering Technology Program
Department of Engineering & Design

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For senior capstone teams, team composition is one of the primary factors in student satisfaction and project success. Previous team formation were done manually after students submitted their top five choices from the available projects and were time consuming and ineffective. To improve team composition and reduce formation time, mixed-integer linear programming is utilized to optimize the team formation process. The presented approach allows control of team size (individually and globally), the number of total teams, assignment of specific students to a particular project, and whether a specific project is a “Go” or “No-go”. An online survey is used to collect each student’s top choices and the results are used to select the projects that will move forward and optimize the team compositions based on student preference. To accomplish this, a team formation score is calculated for possible sets of teams by quantifying the average student “happiness” as measured by how close students get to their first choice. Many possible team compositions are then tested until the optimum teams and projects are found. In application, with class sizes of 60 or 30 students and an average team size of 6, the algorithm was utilized in eight capstone sections to form 40 projects teams using 230 students. Of these students, 74% got their first choice and 94% got one of their top 3 choices. Another key result is a reduction in team formation time from 2 days or so down to less than 1 hour.

Michaelis, B. M., & Bae, H. A. (2019, June), Optimizing Capstone Team Selection Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33148

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