Forming effective teams for large classes is a challenge for educators due to the complexity of project needs, the diversity of individual characteristics, and the criteria different educators have for forming teams. In previous work, we proposed a decision-theoretic algorithm that achieves competitive benchmarking results while scaling the performance to handle large class sizes and a large number of constraints. The algorithm was embedded in a web tool and piloted in a variety of classes. The variety of interdisciplinary use cases led to additional research activities, such as a new team formation algorithm that maximizes social preferences, a new team formation algorithm that considers diversity and minimizes minority tokenism, analytics to support ongoing monitoring of team health, and preliminary analysis of communication behavioral differences in gender-diverse and racially diverse teams. I will present our work and talk about future studies to examine how team formation strategies are used in the classroom.
Dr. Bowen Hui is an Associate Professor of Teaching in Computer Science at the University of British Columbia (Okanagan campus). She has been developing novel learning technologies that maximize student learning outcomes in Computer Science education contexts. Recently, her lab built a team formation and analytics tool called Teamable Analytics, which accommodates a variety of team formation scenarios and criteria. The software has been used in over 33 interdisciplinary classes across the university and it won the best demonstration award at the International Learning Analytics and Knowledge (LAK) conference in 2022. Currently, Dr. Hui's lab is working on extending this tool with team analytics for monitoring to support effective teamwork. Dr. Hui received the UBC Provost’s Award for Teaching Excellence and Innovation in 2022, the Computer Science Canada Award for Excellence in Teaching in 2023, and the UBC Open Educational Resource Excellence and Impact Award in 2024.