LIFT: Integrating stakeholder voices into algorithmic team formation

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 evaluate 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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Hastings, Emily Marie

Contributors dc:contributor

• Bailey, Brian P.
• Karahalios, Karrie

• Algorithms
• CATME
• Learnersourcing
• Crowdsourcing
• Team formation
• Team composition

Statement dc:rights

• Copyright 2019 Emily Marie Hastings

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/104866

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/104866