A core component of team based learning is the creation and maintenance of teams. Plenty of research has demonstrated the potential benefits of collaborative work with a diverse cross-section of individuals. After interviewing (potential and current) employers of Kinesiology program graduates, we identified that communicative ability and teamwork is an important and necessary skill we may not be adequately developing in our student body. This was one of my primary motivations to refine how I assemble teams. Initially under the mentorship of Ann Smiley-Oyen, I created increasingly nuanced mechanisms of team formation, ultimately settling on the process outlined below.
Currently, I rely on building teams of 5-7 students. Too few of students, and the potential diversity benefits wane; too many, and the teams become too unwieldy for everyone to participate. I aim for six students per team, which allows for the errant additional student and the potential of students dropping the course. By intentionally constructing the teams, I mitigate the risk of multiple drops on a single team. Surveys of my students also indicate that the teams themselves ward against students potentially dropping the course for reasons beyond extenuating circumstances. This reinforces the potential value of careful team construction, and the need for these teams to be permanent.
In order to construct effective, inclusive, and diverse teams, I sort students following some basic rules: (1) Diversify the teams based on career aspirations. (2) Do not isolate students with identifying demographics. (3) Make sure every team has at least one teammate with important prerequisite backgrounds. Because Kinesiology is a relatively flexible degree field with many potential career directions, the first rule is easier to implement than many would expect. It can be dangerous when teams have homogeneous career aspirations, as they quickly become echo chambers lacking innovation to challenging problems. Dr. Smiley-Oyen described learning rule two the hard way; isolating genders or race/ethnicity is a short path to an alienated teammate who may not participate. Finally, rule three provides a great shortcut to requiring additional prerequisite courses or technologies (such as smartphones or laptops) that may not be available to all.
I originally sorted students by having them fill out a short paper survey, and then spent hours attempting to create the perfect combination of students for each team. But it's the 21st century - now I accomplish this through a quick background survey at the beginning of the semester; example here. With my smaller class sizes, manually sorting students into teams is easy enough. But with larger classes, I rely on more advanced sorting strategies (such as anti-clustering and re-optimization in R). These techniques are imperfect, but provide a great head-start to sorting and refining teams. An example of this technique in R and dataset from a survey are available.
This survey is also a great avenue to identify other important information about students. For example, alternative names, preferred pronouns, and private considerations can all be optionally prompted for in the survey, providing students a low-pressure opportunity to communicate their preferences or concerns. The survey can also be a vehicle to identify other important access limitations that students may have (such as internet access or long commute times in winter weather) that may impact their capability to participate in the course (and in the team).