FERN: Fair Team Formation for Mutually Beneficial Collaborative Learning

TL;DR

FERN is a novel approach for fair team formation in collaborative learning environments that ensures mutual benefit and fairness across protected attributes, addressing challenges specific to educational settings.

Contribution

Introduces FERN, a multi-objective optimization-based heuristic for fair team formation that promotes mutually beneficial learning and fairness in educational and collaborative contexts.

Findings

FERN outperforms existing team formation methods in fairness and effectiveness.

The problem is NP-hard, and the proposed heuristic demonstrates practical efficiency.

Experimental results validate FERN's ability to promote fairness and mutual benefit.

Abstract

Automated Team Formation is becoming increasingly important for a plethora of applications in open source community projects, remote working platforms, as well as online educational systems. The latter case, in particular, poses significant challenges that are specific to the educational domain. Indeed, teaming students aims to accomplish far more than the successful completion of a specific task. It needs to ensure that all members in the team benefit from the collaborative work, while also ensuring that the participants are not discriminated with respect to their protected attributes, such as race and gender. Towards achieving these goals, this work introduces FERN, a fair team formation approach that promotes mutually beneficial peer learning, dictated by protected group fairness as equality of opportunity in collaborative learning. We formulate the problem as a multi-objective discrete optimization problem. We show this problem to be NP-hard and propose a heuristic hill-climbing algorithm. Extensive experiments on both synthetic and real-world datasets against well-known team formation techniques show the effectiveness of the proposed method.