O uso de algoritmos evolutivos para a formação de grupos na aprendizagem colaborativa no contexto corporativo

• Ano de defesa: 2013
• Autor(a) principal: Caetano, Samuel Sabino
• Orientador(a): Ferreira, Deller James
• Banca de defesa: Soares, Telma Woerle de Lima, Martinhon, Carlos Alberto de Jesus
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Algoritmos Genéticos Híbridos; CSCL; CSCW; CSCL@Work; Meta-Heurística; Heurística; Formação de grupos; Criação do conhecimento organizacional
• Palavras-chave em Inglês: Algoritmos genéticos; Hybrid genetic algorithms; MetaHeuristics; Heuristic; Groups formation; Organizational knowledge creation; Genetic algorithms
• Área do conhecimento CNPq: TEORIA DA COMPUTACAO::ANALISE DE ALGORITMOS E COMPLEXIDADE DE COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/3195

Increasingly, learning in groups has become present in school environments. This fact is also part of the organizations, when considers learning in the workplace. Conscious of the importance of group learning at the workplace (CSCL@Work) emerges as an application area. In Computer Supported Collaborative Learning(CSCL), researchers have been struggling to maximize the performance of groups by techniques for forming groups. Is that why this study developed three (3) algorithmic approaches to formation of intraheterogeneous and inter-homogeneous groups, as well as a model proposed in this work in which integrates dichotomous functional characteristics and preferred roles. We made an algorithm that generates random groups, a Canonical Genetic Algorithm and Hybrid Genetic Algorithm. We obtained the input data of the algorithm by a survey conducted at the Court of the State of Goiás to identify dichotomous functional characteristics, and after we categorize these characteristics, based on the data found and the model proposed group formation. Starting at real data provided of employees whom participated in a course by Distance Education (EaD), we apply the model and we obtained the input data related to functional features. As regards the favorite roles, we assigned randomly values to the employees aforementioned, from a statistical statement made by Belbin into companies in the United Kingdom. Then, we executed the algorithms in three test cases, one considering the preferred papers and functional characteristics, while the other two separately considering each of these perspectives. Based on the results obtained, we found that the hybrid genetic algorithm outperforms the canonical genetic algorithm and random generator.