Hammond, M., & Martinez, J., & Herzog, J. B. (2023, June), Work in Progress: An optimization model for assigning students to multidisciplinary teams by considering preferences and skills  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44448

\bibitem{asee_peer_44448}
  Hammond, M., \& Martinez, J., \& Herzog, J. B. (2023, June), \emph{Work in Progress: An optimization model for assigning students to multidisciplinary teams by considering preferences and skills}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--44448

Megan Hammond, Joan Martinez, and Joseph B. Herzog.  "Work in Progress: An optimization model for assigning students to multidisciplinary teams by considering preferences and skills".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/44448 Internet.  29 Jun, 2024

\bibitem{asee_peer_44448}
  Megan Hammond, Joan Martinez, and Joseph B. Herzog.
  "Work in Progress: An optimization model for assigning students to multidisciplinary teams by considering preferences and skills".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/44448 Internet.  29 Jun, 2024

@INPROCEEDINGS{asee_peer_44448
author = "Megan Hammond, Joan Martinez, and Joseph B. Herzog"
title = "Work in Progress: An optimization model for assigning students to multidisciplinary teams by considering preferences and skills"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/44448}
number = {10.18260/1-2--44448}
}

TY  - CPAPER
AB  - Project-based learning has become popular and prevalent across higher education. Additionally, the Accrediting Board for Engineering and Technology also emphasizes the ability to function in multidisciplinary teams. These educational practices have resulted in the implementation of team-based projects throughout engineering curriculums. Team formation, however, is not a trivial process and occasionally can result in conflict or issues when completing project tasks. At our institution, we noticed that student interest level in a project topic/application is a significant factor toward commitment and contribution to project completion. 

Our institution’s senior capstone requires students to participate in design projects where they are members of multidisciplinary teams solving open-ended real-world problems. Assigning students to projects can be a complicated process, especially considering student preferences, majors, skills, and the needs/nature of the project. We are a young program continuing to grow and are interested in a systematic approach to assign teams. Currently, a rank-based survey is used to gauge student interest in each individual project for assignment purposes. Faculty leaders consider students' ranking of the projects and the project needs to assign student teams. While we consider our current assignment method effective, it is a manual, time-intensive, and highly iterative process.

This paper presents a work-in-progress of a new assignment method using weight-based integer programming techniques. Some of the considerations for assignment weights and constraints include student preferences, student technical skill sets, team sizes, and faculty input. A comparative analysis between our proposed optimization model and the current assignment method is shown. Discussions of the similarities and differences between these two assignment methods are also presented.

AU  - Megan Hammond
AU  - Joan Martinez
AU  - Joseph B. Herzog
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Work in Progress: An optimization model for assigning students to multidisciplinary teams by considering preferences and skills
UR  - https://peer.asee.org/44448
DO  - 10.18260/1-2--44448
ER  -