Synergistic team composition: A computational approach to foster diversity in teams

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.

In search of diverse and connected teams: A computational approach to assemble diverse teams based on members’ social networks

“…Finally, this work provides more computational approaches to enrich team formation processes [45,87]. Since team builders cannot solve this problem quickly by manually checking each team combination, algorithms can automatize this task by bringing together members who possess existing social connections and, at the same time, have different backgrounds, characteristics, and expertise levels [41,42]. We expect this work will assist in forming heterogeneous teams by considering diversity and social networks.…”

“…Practical implications of this study contribute to several communities invested in increasing team diversity. Since team builders cannot solve this problem quickly by manually checking each team combination, algorithms can automatize this task by bringing together members who possess existing social connections while, at the same time, from different backgrounds, characteristics, and expertise levels [41,42]. Expanding the use of this algorithm to broader audiences could provide new benefits for groups that seek to embrace diversity and keep high familiarity levels.…”

TAIP: an anytime algorithm for allocating student teams to internship programs

“…That is, we need to assign to each student s ∈ K a subset of competencies of C p , and assume that student s is responsible for (in charge of) their assigned competencies. According to [5] we have that:…”

“…[7] propose several heuristic algorithms for forming a single robust team in order to compete a given set of tasks. The authors in [5] target the problem of partitioning a group of individuals into equal-sized teams so that each one will resolve the same task. Here we consider the problem of allocating individuals into teams of different sizes in order to resolve different tasks.…”

“…Here we consider the problem of allocating individuals into teams of different sizes in order to resolve different tasks. In fact, our problem can be viewed as a generalization of [5].…”