Synergistic Team Composition: A Computational Approach to Foster Diversity in Teams

TL;DR

This paper introduces the synergistic team composition problem (STCP), a novel computational approach to forming diverse, balanced teams based on personality, gender, and competencies, with algorithms tested on real-world educational data.

Contribution

The paper formulates the STCP, proposes two algorithms for solving it, and evaluates their effectiveness in educational team formation scenarios.

Findings

Linear programming algorithm effective for small instances

Heuristic algorithm scalable for large instances

Algorithms produce diverse, balanced teams in educational data

Abstract

Co-operative learning in heterogeneous teams refers to learning methods in which teams are organised both to accomplish academic tasks and for individuals to gain knowledge. Competencies, personality and the gender of team members are key factors that influence team performance. Here, we introduce a team composition problem, the so-called synergistic team composition problem (STCP), which incorporates such key factors when arranging teams. Thus, the goal of the STCP is to partition a set of individuals into a set of synergistic teams: teams that are diverse in personality and gender and whose members cover all required competencies to complete a task. Furthermore, the STCP requires that all teams are balanced in that they are expected to exhibit similar performances when completing the task. We propose two efficient algorithms to solve the STCP. Our first algorithm is based on a linear programming formulation and is appropriate to solve small instances of the problem. Our second algorithm is an anytime heuristic that is effective for large instances of the STCP. Finally, we thoroughly study the computational properties of both algorithms in an educational context when grouping students in a classroom into teams using actual-world data.