Effectiveness Factors for Algorithm Based Team Formation with Data Project Case Application

Teams must be formed for all kinds of projects and purposes. Team formation is a key activity for innovation, entrepreneurship, class projects, and industry initiatives. In parallel work, we have proposed a generalized framework for Algorithm-based Team Formation. We are interested to apply this framework to the specific task of forming teams for a Data Science project course. In this paper, we have focused on the characteristics teams as correlated by the success of the project itself. We find that by examining approximately 30 project teams, there are characteristics which may be used to set feature values for algorithms that can best match student projects together. In particular, teams who work well together with trust and common backgrounds did perform better. Teams with greater domain experience in coding and ML generally also performed better. Surprisingly, diversity in background did not seem to indicate better performance. However, we did observe that individuals who characterize themselves as optimistic and wide comfort zone among others were more strongly present on teams that performed well, speaking to a correlation of individual characteristics/behaviors to team performance.