Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/197706
COMPARTIR / EXPORTAR:
logo share SHARE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE

Invitar a revisión por pares abierta
Título

Heterogeneous teams for homogeneous performance

AutorAndrejczuk, Ewa; Bistaffa, Filippo CSIC ORCID ; Blum, Christian CSIC ORCID ; Rodríguez-Aguilar, Juan Antonio CSIC ORCID CVN ; Sierra, Carles CSIC ORCID
Fecha de publicación2018
EditorElsevier
CitaciónPRIMA 2018: Principles and Practice of Multi-Agent Systems: 89-105 (2018)
ResumenCo-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.
DescripciónTrabajo presentado en 21st International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2018), celebrado en Tokio (Japón), del 29 de octubre al 2 de noviembre de 2018
Versión del editorhttp://dx.doi.org/10.1007/978-3-030-03098-8_6
URIhttp://hdl.handle.net/10261/197706
DOI10.1007/978-3-030-03098-8_6
ISBN978-3-030-03097-1
Aparece en las colecciones: (IIIA) Libros y partes de libros




Ficheros en este ítem:
Fichero Descripción Tamaño Formato
accesoRestringido.pdf15,35 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo

CORE Recommender

Page view(s)

185
checked on 18-abr-2024

Download(s)

28
checked on 18-abr-2024

Google ScholarTM

Check

Altmetric

Altmetric


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.