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dc.contributor.authorEwa Andrejczukes_ES
dc.contributor.authorBistaffa, Filippoes_ES
dc.contributor.authorBlum, Christianes_ES
dc.contributor.authorJuan A. Rodríguez-Aguilares_ES
dc.contributor.authorCarles Sierraes_ES
dc.date.accessioned2020-01-13T12:36:44Z-
dc.date.available2020-01-13T12:36:44Z-
dc.date.issued2018-
dc.identifier.citationPRIMA 2018: Principles and Practice of Multi-Agent Systems: 89-105 (2018)es_ES
dc.identifier.isbn978-3-030-03097-1-
dc.identifier.urihttp://hdl.handle.net/10261/197706-
dc.descriptionTrabajo 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 2018es_ES
dc.description.abstractCo-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.es_ES
dc.publisherElsevieres_ES
dc.rightsclosedAccesses_ES
dc.titleHeterogeneous teams for homogeneous performancees_ES
dc.typecomunicación de congresoes_ES
dc.identifier.doi10.1007/978-3-030-03098-8_6-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-030-03098-8_6es_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
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