2024 ASEE Annual Conference & Exposition

This evidence-based research paper presents a new approach to team formation in engineering courses. Teamwork plays a pivotal role in active learning and holds the potential to enhance the inclusivity and equity of educational environments. However, engineering students hailing from diverse backgrounds with varying skills, experiences, and learning preferences pose challenges in forming balanced and cohesive teams. While some students exhibit strong preferences for team members, others are open to diverse collaborations. Accommodating these preferences during team formation is a complex task.
This paper discusses a novel approach to team formation, shifting from random, self, and student feature-based selection to purposeful grouping to maximize new connections between students. An example is provided to demonstrate the different team formation variations:
• Maximizing New Connections: Encouraging students to collaborate with as many unfamiliar peers as possible.
• Pre-assigning Students: Satisfying students' strong preferences or need to be on a specific team.
• Balancing Teams: Consideration of specific student characteristics, such as forming diverse teams.
• Forcing Teammates: Ensuring specific students collaborate on a team as needed.
• Averting Teammates: Allowing students to express their preferences to avoid certain teammates.

By intentionally constructing teams to circumvent pre-existing intra-team connections, this method significantly fosters the formation of novel connections and therefore enriches the learning experience for students. Examples of how to implement the approach using a spreadsheet and a freely available solver plug-in are presented, including different team formation scenarios.
In summary, the presented team formation method opens new avenues for engineering education, offering a practical strategy to enhance teamwork, foster inclusivity, and expedite network expansion among students. Since the method is based on mathematical optimization, it eliminates biases and is therefore ideal for engineering educational research projects.