Fair Senior Capstone Project Teaming Based on Skills, Preferences, and Friend Groups

Zachary Nolan Sunberg; Kathryn Anne Wingate; Lara Buri

Download Paper | Permalink

Conference: 2021 ASEE Virtual Annual Conference Content Access
Location: Virtual Conference
Publication Date: July 26, 2021
Start Date: July 26, 2021
End Date: July 19, 2022
Conference Session: Teaching Methodology & Assessment 1
Tagged Division: Aerospace
Page Count: 16
DOI: 10.18260/1-2--37187
Permanent URL: https://peer.asee.org/37187
Download Count: 388

Paper Authors

biography

Zachary Nolan Sunberg University of Colorado Boulder orcid.org/0000-0001-9707-3035

visit author page

Zachary Sunberg is an Assistant Professor in the Ann and H.J. Smead Aerospace Engineering Sciences Department. He earned Bachelors and Masters degrees in Aerospace Engineering from Texas A&M University and a PhD in Aeronautics and Astronautics at Stanford University. Before joining the University of Colorado faculty, he served as a postdoctoral scholar at the University of California, Berkeley. His research is focused on decision making under uncertainty to enable safe and efficient autonomous vehicle operation.

visit author page

biography

Kathryn Anne Wingate University of Colorado Boulder

visit author page

Dr. Kathryn Wingate is an instructor at University of Colorado Boulder, where she teaches design and mechanics courses. She holds her PhD in mechanical engineering, and worked at NGAS as a materials scientist.

visit author page

biography

Lara Buri University of Colorado, Boulder

visit author page

Lara Buri is a graduate student at the University of Colorado, Boulder, where she is working on her master's degree in fluids, structures, and materials. She previously earned her bachelor's degree in aerospace engineering at CU Boulder and has been a teaching assistant for multiple classes in the aerospace engineering program.

visit author page

Download Paper | Permalink

Abstract

Capstone design courses are critical pedagogical components of any engineering curriculum as they allow students to complete open ended projects in a team setting, often while interacting with industry customers. Equitably teaming students for these courses can be a difficult challenge. Each team must have the technical and leadership skills necessary to complete the project, and industry sponsors prefer to have high performing students for recruiting purposes. Moreover, students often have strong preferences based on which technical challenges excite them, and often desire to work with the friends that they have developed close relationships with in other courses. Large universities often have hundreds of students enrolled in these courses, making hand-design of teams intractable.

In past years, the Aerospace Engineering Department at our university has allowed students to choose their own teams during the second meeting of the class. While this allows a great deal of freedom to students, many end up assigned to projects that they find undesirable. Further, this methodology inadvertently resulted in the formation of student cliques and peer to peer conflict, which then may have damaged team dynamics and performance throughout the year. In fall 2020, the COVID-19 pandemic precluded the usual teaming method, and the project advisory board adopted an optimization-based fair rank-maximal allocation approach.

There have been many other similar optimization approaches proposed in the literature with objectives ranging from balancing team grade-point averages to distributing Meyers-Briggs personality types, and solution approaches ranging from evolutionary algorithms to mixed integer programming. The optimization objective for our approach has 3 components. First, each project has minimum requirements for the number of students with electronics, software, hardware, and project management skills. Second, to allow students to work with friends and avoid isolating minoritized students in uncomfortable situations, they were allowed to designate one or two other students with whom they would be assigned. Finally we attempted to find a fair rank-maximal allocation based on the students’ ranked preferences for projects. This optimization problem was formulated as a mixed integer linear program, expressed using the Julia programming language’s JuMP optimization framework and solved with the Gurobi optimizer.

The resulting team assignments were very successful, as each student was assigned to one of their top three project choices. In addition to discussing the mathematical details and limitations of the approach, we plan to analyze two research questions:

1) Did teams this year perform better than teams in the past in terms of grades in the preliminary and comprehensive design reviews? 2) Was there a notable difference in the team dynamics this year compared to past years?

To examine team dynamics, peer evaluations from this year (including comments) will be compared to those from the past two academic years. Limitations of these methods will be acknowledged, particularly as COVID-19 could severely impact how students are interacting in teams. Finally, the code will be made available online as an MIT-licensed open source package.

Citation
Format

Sunberg, Z. N., & Wingate, K. A., & Buri, L. (2021, July), Fair Senior Capstone Project Teaming Based on Skills, Preferences, and Friend Groups Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--37187

TY - CPAPER
AB - Capstone design courses are critical pedagogical components of any engineering curriculum as they allow students to complete open ended projects in a team setting, often while interacting with industry customers. Equitably teaming students for these courses can be a difficult challenge. Each team must have the technical and leadership skills necessary to complete the project, and industry sponsors prefer to have high performing students for recruiting purposes. Moreover, students often have strong preferences based on which technical challenges excite them, and often desire to work with the friends that they have developed close relationships with in other courses. Large universities often have hundreds of students enrolled in these courses, making hand-design of teams intractable.

In past years, the Aerospace Engineering Department at our university has allowed students to choose their own teams during the second meeting of the class. While this allows a great deal of freedom to students, many end up assigned to projects that they find undesirable. Further, this methodology inadvertently resulted in the formation of student cliques and peer to peer conflict, which then may have damaged team dynamics and performance throughout the year. In fall 2020, the COVID-19 pandemic precluded the usual teaming method, and the project advisory board adopted an optimization-based fair rank-maximal allocation approach.

There have been many other similar optimization approaches proposed in the literature with objectives ranging from balancing team grade-point averages to distributing Meyers-Briggs personality types, and solution approaches ranging from evolutionary algorithms to mixed integer programming. The optimization objective for our approach has 3 components. First, each project has minimum requirements for the number of students with electronics, software, hardware, and project management skills. Second, to allow students to work with friends and avoid isolating minoritized students in uncomfortable situations, they were allowed to designate one or two other students with whom they would be assigned. Finally we attempted to find a fair rank-maximal allocation based on the students’ ranked preferences for projects. This optimization problem was formulated as a mixed integer linear program, expressed using the Julia programming language’s JuMP optimization framework and solved with the Gurobi optimizer.

The resulting team assignments were very successful, as each student was assigned to one of their top three project choices. In addition to discussing the mathematical details and limitations of the approach, we plan to analyze two research questions:

1) Did teams this year perform better than teams in the past in terms of grades in the preliminary and comprehensive design reviews?
2) Was there a notable difference in the team dynamics this year compared to past years?

To examine team dynamics, peer evaluations from this year (including comments) will be compared to those from the past two academic years. Limitations of these methods will be acknowledged, particularly as COVID-19 could severely impact how students are interacting in teams. Finally, the code will be made available online as an MIT-licensed open source package.

AU - Zachary Nolan Sunberg
AU - Kathryn Anne Wingate
AU - Lara Buri
CY - Virtual Conference
DA - 2021/07/26
PB - ASEE Conferences
TI - Fair Senior Capstone Project Teaming Based on Skills, Preferences, and Friend Groups
UR - https://peer.asee.org/37187
DO - 10.18260/1-2--37187
ER -