Composing Team Compositions: An Examination of Instructors' Current Algorithmic Team Formation Practices

Instructors using algorithmic team formation tools must decide which criteria (e.g., skills, demographics, etc.) to use to group students into teams based on their teamwork goals, and have many possible sources from which to draw these configurations (e.g., the literature, other faculty, their students, etc.). However, tools offer considerable flexibility and selecting ineffective configurations can lead to teams that do not collaborate successfully. Due to such tools' relative novelty, there is currently little knowledge of how instructors choose which of these sources to utilize, how they relate different criteria to their goals for the planned teamwork, or how they determine if their configuration or the generated teams are successful. To close this gap, we conducted a survey (N=77) and interview (N=21) study of instructors using CATME Team-Maker and other criteria-based processes to investigate instructors' goals and decisions when using team formation tools. The results showed that instructors prioritized students learning to work with diverse teammates and performed "sanity checks" on their formation approach's output to ensure that the generated teams would support this goal, especially focusing on criteria like gender and race. However, they sometimes struggled to relate their educational goals to specific settings in the tool. In general, they also did not solicit any input from students when configuring the tool, despite acknowledging that this information might be useful. By opening the "black box" of the algorithm to students, more learner-centered approaches to forming teams could therefore be a promising way to provide more support to instructors configuring algorithmic tools while at the same time supporting student agency and learning about teamwork.

CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing; Empirical studies in HCI.