FERN: Fair Team Formation for Mutually Beneficial Collaborative Learning

Maria Kalantzi, Agoritsa Polyzou, George Karypis

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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 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 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 the synthetic and real-world datasets against well-known team formation techniques show the effectiveness of the proposed method.

Original languageEnglish (US)
Pages (from-to)757-770
Number of pages14
JournalIEEE Transactions on Learning Technologies
Issue number6
StatePublished - Dec 1 2022

Bibliographical note

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  • Collaborative learning
  • group fairness
  • hill climbing
  • partitioning
  • team formation


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