LIFT: Integrating Stakeholder Voices into Algorithmic Team Formation

Emily M. Hastings, Albatool Alamri, Andrew Kuznetsov, Christine Pisarczyk, Karrie Karahalios, Darko Marinov, Brian P. Bailey
2020 Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems  
Team formation tools assume instructors should configure the criteria for creating teams, precluding students from participating in a process that affects their learning experience. We propose LIFT, a novel learner-centered workflow where students propose, vote for, and weigh team formation criteria, and the collective results serve as inputs to the team formation algorithm. We conducted an experiment (N=289) comparing LIFT to the usual instructor-led process, and interviewed participants to
more » ... luate their perceptions of LIFT and its outcomes. We found learners were capable of proposing novel criteria not part of existing algorithmic tools, like organizational style. Generally, learners avoided criteria frequently selected by instructors, including gender and GPA, and instead preferred those that promoted efficient collaboration. Second, LIFT led to team outcomes comparable to those achieved by the instructor-led approach, despite the differences in the configurations, and teams valued having control of the team formation process. We provide instructors and tool designers with a workflow and evidence supporting giving learners control of the algorithmic process used for grouping them into teams. ii To my parents, Kevin and Gay Lynn, for their unending love and support. iii ACKNOWLEDGMENTS
doi:10.1145/3313831.3376797 dblp:conf/chi/HastingsAKPKMB20 fatcat:e2badct4yrehlgwftr4k4anymm