Utilização de Algoritmos Genéticos no Processo de Formação de Equipes Heterogêneas em Turmas Universitárias
Resumo
A alocação de alunos em equipes é uma das formas utilizadas na Educação para favorecer a aprendizagem colaborativa através da troca de conhecimentos entre indivíduos. No entanto, realizar a distribuição dos alunos em equipes heterogêneas ainda é um problema relacionado a essa abordagem, dada a quantidade de combinações possíveis. Este trabalho propõe uma solução baseada em algoritmos genéticos para otimizar o processo de formação de equipes heterogêneas em ambiente universitário. Os experimentos realizados demonstram que a estratégia proposta foi capaz de maximizar a heterogeneidade nas equipes de forma mais eficaz do que os métodos tradicionais comumente utilizados para essa atividade.
Palavras-chave:
Algoritmos Genéticos, Equipes Heterogêneas, Educação, Aprendizagem Colaborativa
Referências
Balieiro, A., Melo, I., Araújo, D., Neto, S., Galdino, E., and Gomes, A. (2017). Uma abordagem baseada em algoritmo genético para formação de grupos de estudos em ambientes virtuais de aprendizagem. In Simpósio Brasileiro de Informática na Educação - SBIE, pages 1287–1296.
Bandyopadhyay, S. and Maulik, U. (2002). Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition, 35(6):1197–1208.
Blum, C. and Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys (CSUR), 35(3):268–308.
Cen, L., Ruta, D., Powell, L., and Ng, J. (2015). Interaction driven composition of student groups for optimal group work learning performance. In Proceedings of the 2015 IEEE Frontiers in Education Conference (FIE), pages 1–6, Washington, DC, USA. IEEE Computer Society.
Chen, L. and Yang, Q. H. (2014). A group division method based on collaborative learning elements. In The 26th Chinese Control and Decision Conference (2014 CCDC), pages 1701–1705.
Coley, D. A. (1999). An introduction to genetic algorithms for scientists and engineers. World Scientific Publishing Co Inc.
Cooke, N. J., Gorman, J. C., Myers, C. W., and Duran, J. L. (2013). Interactive team cognition. Cognitive Science, 37(2):255–285.
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA.
Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.
Lin, Y. S., Chang, Y. C., and Chu, C. P. (2016). Novel approach to facilitating trade-off multi-objective grouping optimization. IEEE Transactions on Learning Technologies, 9(2):107–119.
Macintyre, H. and Ireson, J. (2002). Within-class ability grouping: Placement of pupils in groups and self-concept. British Educational Research Journal, 28(2):249–263.
Maldonado Pérez, M. (2007). El trabajo colaborativo en el aula universitaria. Laurus, 13(23).
Mitchell, M. (1998). An introduction to genetic algorithms. MIT press.
Moreno, J., Ovalle, D. A., and Vicari, R. M. (2012). A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics. Computers & Education, 58(1):560–569.
Silva, H. M., Silva, C. A., and Gorgônio, F. L. (2012). A self-organizing map based strategy for heterogeneous teaming. In Johnsson, M., editor, Applications of Self-Organizing Maps, chapter 05. InTech, Rijeka.
Torres, T. Z. and Amaral, S. F. d. (2011). Aprendizagem colaborativa e web 2.0: proposta de modelo de organização de conteúdos interativos. Educação Temática Digital, 12:49.
Zheng, Y., Liu, Y., Lu, W., and Li, C. (2016). A hybrid pso-ga method for composing heterogeneous groups in collaborative learning. In 2016 11th International Conference on Computer Science Education (ICCSE), pages 160–164.
Bandyopadhyay, S. and Maulik, U. (2002). Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition, 35(6):1197–1208.
Blum, C. and Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys (CSUR), 35(3):268–308.
Cen, L., Ruta, D., Powell, L., and Ng, J. (2015). Interaction driven composition of student groups for optimal group work learning performance. In Proceedings of the 2015 IEEE Frontiers in Education Conference (FIE), pages 1–6, Washington, DC, USA. IEEE Computer Society.
Chen, L. and Yang, Q. H. (2014). A group division method based on collaborative learning elements. In The 26th Chinese Control and Decision Conference (2014 CCDC), pages 1701–1705.
Coley, D. A. (1999). An introduction to genetic algorithms for scientists and engineers. World Scientific Publishing Co Inc.
Cooke, N. J., Gorman, J. C., Myers, C. W., and Duran, J. L. (2013). Interactive team cognition. Cognitive Science, 37(2):255–285.
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA.
Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.
Lin, Y. S., Chang, Y. C., and Chu, C. P. (2016). Novel approach to facilitating trade-off multi-objective grouping optimization. IEEE Transactions on Learning Technologies, 9(2):107–119.
Macintyre, H. and Ireson, J. (2002). Within-class ability grouping: Placement of pupils in groups and self-concept. British Educational Research Journal, 28(2):249–263.
Maldonado Pérez, M. (2007). El trabajo colaborativo en el aula universitaria. Laurus, 13(23).
Mitchell, M. (1998). An introduction to genetic algorithms. MIT press.
Moreno, J., Ovalle, D. A., and Vicari, R. M. (2012). A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics. Computers & Education, 58(1):560–569.
Silva, H. M., Silva, C. A., and Gorgônio, F. L. (2012). A self-organizing map based strategy for heterogeneous teaming. In Johnsson, M., editor, Applications of Self-Organizing Maps, chapter 05. InTech, Rijeka.
Torres, T. Z. and Amaral, S. F. d. (2011). Aprendizagem colaborativa e web 2.0: proposta de modelo de organização de conteúdos interativos. Educação Temática Digital, 12:49.
Zheng, Y., Liu, Y., Lu, W., and Li, C. (2016). A hybrid pso-ga method for composing heterogeneous groups in collaborative learning. In 2016 11th International Conference on Computer Science Education (ICCSE), pages 160–164.
Publicado
11/11/2019
Como Citar
SILVA, Yuri Kelvin N.; GUERRA, Fabrício V. A.; GORGÔNIO, Flavius L..
Utilização de Algoritmos Genéticos no Processo de Formação de Equipes Heterogêneas em Turmas Universitárias. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
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p. 1866-1875.
DOI: https://doi.org/10.5753/cbie.sbie.2019.1866.
