TitleHeterogeneous Teams for Homogeneous Performance
Publication TypeConference Paper
Year of Publication2018
AuthorsAndrejczuk E, Bistaffa F, Blum C, Rodríguez-Aguilar JA, Sierra C
Conference NameProceedings of the 21st International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2018)
PublisherSpringer LNCS
Keywordsapproximation algorithms, Team Composition

Co-operative learning is used to refer to learning procedures for heterogeneous teams in which individuals and teamwork are organised to complete academic tasks. Key factors of team performance are competencies, personality and gender of team members. Here, we present a computational model that incorporates these key factors to form heterogeneous teams. In addition, we propose efficient algorithms to partition a classroom into teams of even size and homogeneous performance. The first algorithm is based on an ILP formulation. For small problem instances, this approach is appropriate. However, this is not the case for large problems for which we propose a heuristic algorithm. We study the computational properties of both algorithms when grouping students in a classroom into teams.