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Previous issue date: 2013-09-09 / 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. / A aprendizagem em grupos tem se tornado realidade cada vez mais presente nos ambientes
de ensino. Esta realidade também faz parte das organizações quando considera-se
a aprendizagem no contexto do trabalho. Cientes da importância da aprendizagem em
grupo no ambiente de trabalho, uma nova abordagem, denominada CSCL@Work, surge
como uma aplicação da área Aprendizagem Colaborativa Apoiada pelo Computador, no
inglês, Computer Supported Collaborative Learning (CSCL), no ambiente de trabalho.
Em CSCL, pesquisadores tem se esforçado cada vez mais para maximizar o desempenho
dos grupos através de técnicas para formação de grupos. Por isso neste trabalho desenvolvemos
3 (três) abordagens algorítmicas para formação de grupos intra-heterogêneos e
inter-homogêneos, a partir de um modelo proposto nesta pesquisa, que integra características
funcionais dicotômicas e papéis preferidos. Confeccionamos um algoritmo que gera
grupos aleatoriamente, um algoritmo genético canônico e um algoritmo genético híbrido.
Para obter os dados de entrada do algoritmo, realizamos uma pesquisa no Tribunal de
Justiça do Estado de Goiás para identificar características funcionais dicotômicas, categorizamos
estas características, com base nos dados encontrados e no modelo de formação
de grupos proposto. A partir de dados reais fornecidos de funcionários que participaram de
um curso por Educação a Distância (EaD), aplicamos o modelo e obtivemos os dados de
entrada relativos às características funcionais. Quanto aos papéis preferidos, atribuímos
os valores aleatoriamente aos funcionários mencionados, partindo de um levantamento
estatístico feito por Belbin em empresas no Reino Unido. Em seguida, executamos os algoritmos
em três casos de testes, um considerando as características funcionais e papéis
preferidos, e os outros dois considerando separadamente cada uma destas perspectivas. A
partir dos resultados obtidos, constatamos que o algoritmo genético híbrido obtém resultados
superiores ao algoritmo genético canônico e método aleatório.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.bc.ufg.br:tede/3195 |
| Date | 09 September 2013 |
| Creators | Caetano, Samuel Sabino |
| Contributors | Ferreira, Deller James, Camilo Junior, Celso Gonçalves, Soares, Telma Woerle de Lima, Martinhon, Carlos Alberto de Jesus |
| Publisher | Universidade Federal de Goiás, Programa de Pós-graduação em Ciência da Computação (INF), UFG, Brasil, Instituto de Informática - INF (RG) |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Biblioteca Digital de Teses e Dissertações da UFG, instname:Universidade Federal de Goiás, instacron:UFG |
| Rights | http://creativecommons.org/licenses/by-nc-nd/4.0/, info:eu-repo/semantics/openAccess |
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