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