FERN: Fair Team Formation for Mutually Beneficial Collaborative Learning

Maria Kalantzi, Agoritsa Polyzou, George Karypis

Abstract: Automated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">team formation</i> 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 the members in the team benefit from the collaborative work, while also ensuring that the participants are not discriminated against with respect to their protected attributes, such as race and gender. Toward achieving these goals, this article introduces <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FERN</monospace> , a fair team formation approach that promotes mutually beneficial peer learning, dictated by protected group fairness as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">equality of opportunity</i> 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 the synthetic and real-world datasets against well-known team formation techniques show the effectiveness of the proposed method.