June 15, 2019
June 15, 2019
June 19, 2019
This complete, evidence-based practice paper presents a study on a new open-source software tool that was written by the authors for creating optimal student teams. Creating effective teams can be a difficult task for an instructor, especially when creating them from first year students who have yet to establish preferred work habits or preferred teammates. In many cases, instructors of first year courses will form student teams by randomly assigning students to teams, by allowing students to self-select teammates, or, at best, by trying to optimize teams using whatever limited knowledge they have about these new students. Here, we present and analyze gruepr, a new software tool for partitioning up to 200 students into optimal project teams. The tool has been released under an open source license and is freely available. The software tool runs on the instructor’s computer using survey data entered by the students into, and then downloaded from, a Google Form. The instructor has considerable flexibility in choosing the content of the survey questions as well as the definition of a quantitatively optimal team. The optimization problem is computationally difficult, and the search space is immense. For example, there are over 1E19 ways to partition 32 students into 8 teams of 4. Thus, manually assembling a set of teams is time consuming and highly unlikely to provide an optimal result. It is for this reason that gruepr was created. Gruepr uses a genetic algorithm to perform the optimization. In this paper, we first discuss the need for such a program, then describe how an instructor uses gruepr, then present the design of the program, and finally provide analysis of gruepr and results from its recent use by 7 faculty members in the formation of teams within 18 sections of a project-based, first year engineering course.
Hertz, J. L., & Davis, D., & O'Connell, B. P., & Mukasa, C. (2019, June), gruepr: An Open Source Program for Creating Student Project Teams Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. https://peer.asee.org/32880
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