Automatic Group Formation for Large Classes in Distance Education
Resumo
Neste trabalho, um método de formação de grupo foi desenvolvido para ambientes de educação a distância envolvendo grandes turmas, como Moocs, a fim de facilitar as interações entre os alunos. Os princípios de formação de grupo são aplicados como uma tentativa de atender à dicotomia existente entre o coletivo, que envolve a formação de uma comunidade de aprendizagem on-line em grande escala, e o indivíduo, com diferentes interesses, conhecimentos prévios, expectativas e perfis. A formação de grupos é automática, utilizando um algoritmo baseado no método de enxame de partículas e considerando três critérios: nível de conhecimento, interesse e perfil de liderança.
Palavras-chave:
Educação a Distância, Formação de Grupos, Moocs
Referências
Arendale, D. R. and Hane, A. R. (2014). Holistic growth of college peer study group participants: Prompting academic and personal development. Research & Teaching in Developmental Education, 31(1):7.
Chandrasekaran, M. K., Kan, M. Y., Tan, B. C., and Ragupathi, K. (2015). Learning-instructor intervention from mooc forums: Early results and issues. ArXiv preprint arXiv:1504.07206.
Chen, W. N., Zhang, J., Chung, H., Zhong, W. L., gang Wu, W., and Hui Shi, Y. (2010). A novel set-based particle swarm optimization method for discrete optimization problems. Evolutionary Computation, IEEE Transactions on, 14(2): 278–300.
Claros, I. and Leovy Echeverria, R. C. (2015). Towards moocs scenaries based on collaborative learning approaches. In Global Engineering Education Conference (EDUCON), pages 955–958.
Dascalu, M. I., Bodea, C. N., and Burlacu, A. (2013). Platform for creating collaborative e-learning communities based on automated composition of learning groups. In Engineering of Computer Based Systems (ECBS-EERC), 2013 3rd Eastern European Regional Conference on the, pages 103–112.
Dascalu, M. I., Bodea, C. N., Lytras, M., De Pablos, P. O., and Burlacu, A. (2014). Improving e-learning communities through optimal composition of multidisciplinary learning groups. Computers in Human Behavior, 30:362–371.
Davidson, N. and Kroll, D. L. (1991). An overview of research on cooperative learning related to mathematics. Journal for Research in Mathematics Education, pages 362–365.
Harris, A., Jones, M., and Baba, S. (2013). Distributed leadership and digital collaborative learning: A synergistic relationship? British Journal of Educational Technology, 44(6):926–939.
Holliday, T. L. and Said, S. H. (2008). Psychophysiological measures of learning comfort: Study groups’ learning styles and pulse changes. Learning Assistance Review, 13(1):7–16.
Jarboui, B., Damak, N., Siarry, P., and Rebai, A. (2008a). A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems. Applied Mathematics and Computation, 195(1):299–308.
Jarboui, B., Ibrahim, S., Siarry, P., and Rebai, A. (2008b). A combinatorial particle swarm optimisation for solving permutation flowshop problems. Computers & Industrial Engineering, 54(3):526–538.
Karamolegkos, P. N., Patrikakis, C. Z., Doulamis, N. D., Vlacheas, P. T., and Nikolakopoulos, I. G. (2009). An evaluation study of clustering algorithms in the scope of user communities assessment. Computers & Mathematics with Applications, 58(8):1498 1519.
Kennedy, G., Coffrin, C., de Barba, P., and Corrin, L. (2015). Predicting success: how learners’ prior knowledge, skills and activities predict mooc performance. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, pages 136–140. ACM.
Lin, Y. T., Huang, Y. M., and Cheng, S.-C. (2010). An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization. Computers & Education, 55(4):1483–1493.
Linnenbrink-Garcia, L., Pugh, K. J., Koskey, K. L., and Stewart, V. C. (2012). Developing conceptual understanding of natural selection: The role of interest, efficacy, and basic prior knowledge. The Journal of Experimental Education, 80(1):45–68.
McAuley, A., Stewart, B., Siemens, G., and Cormier, D. (2010). The mooc model for digital practice, sshrc knowledge synthesis grant on the digital economy. Recuperado de [link].
Newman, D. R., Webb, B., and Cochrane, C. (1995). A content analysis method to measure critical thinking in face-to-face and computer supported group learning. Interpersonal Computing and Technology, 3(2):56–77.
Northouse, P. G. (2014). Introduction to leadership: Concepts and practice. Sage Publications.
Pedersen, M. E. H. and Chipperfield, A. J. (2010). Simplifying particle swarm optimization. Applied Soft Computing, 10(2):618–628.
Pierobom, J. L., Delgado, M. R., and Kaestner, C. A. (2012). Particle swarm optimization applied to the dynamic allocation problem. In Neural Networks (SBRN), 2012. Brazilian Symposium on, pages 184–189. IEEE.
Robinson, A. C., Kerski, J., Long, E. C., Luo, H., DiBiase, D., and Lee, A. (2015). Maps and the geospatial revolution: teaching a massive open online course (mooc) in geography. Journal of Geography in Higher Education, 39(1):65–82.
Rosendo, M. and Pozo, A. (2010). Applying a discrete particle swarm optimization algorithm to combinatorial problems. In Neural Networks (SBRN), 2010 Eleventh Brazilian Symposium on, pages 235–240.
Wang, S.-Q., Gong, L. H., and Yan, S. L. (2009). The allocation optimization of project human resource based on particle swarm optimization algorithm. In Services Science, Management and Engineering, 2009. SSME ’09. IITA International Conference on, pages 169–172.
Yang, S. J. (2006). Context aware ubiquitous learning environments for peer-to-peer collaborative learning. Journal of Educational Technology & Society, 9(1):188–201.
Chandrasekaran, M. K., Kan, M. Y., Tan, B. C., and Ragupathi, K. (2015). Learning-instructor intervention from mooc forums: Early results and issues. ArXiv preprint arXiv:1504.07206.
Chen, W. N., Zhang, J., Chung, H., Zhong, W. L., gang Wu, W., and Hui Shi, Y. (2010). A novel set-based particle swarm optimization method for discrete optimization problems. Evolutionary Computation, IEEE Transactions on, 14(2): 278–300.
Claros, I. and Leovy Echeverria, R. C. (2015). Towards moocs scenaries based on collaborative learning approaches. In Global Engineering Education Conference (EDUCON), pages 955–958.
Dascalu, M. I., Bodea, C. N., and Burlacu, A. (2013). Platform for creating collaborative e-learning communities based on automated composition of learning groups. In Engineering of Computer Based Systems (ECBS-EERC), 2013 3rd Eastern European Regional Conference on the, pages 103–112.
Dascalu, M. I., Bodea, C. N., Lytras, M., De Pablos, P. O., and Burlacu, A. (2014). Improving e-learning communities through optimal composition of multidisciplinary learning groups. Computers in Human Behavior, 30:362–371.
Davidson, N. and Kroll, D. L. (1991). An overview of research on cooperative learning related to mathematics. Journal for Research in Mathematics Education, pages 362–365.
Harris, A., Jones, M., and Baba, S. (2013). Distributed leadership and digital collaborative learning: A synergistic relationship? British Journal of Educational Technology, 44(6):926–939.
Holliday, T. L. and Said, S. H. (2008). Psychophysiological measures of learning comfort: Study groups’ learning styles and pulse changes. Learning Assistance Review, 13(1):7–16.
Jarboui, B., Damak, N., Siarry, P., and Rebai, A. (2008a). A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems. Applied Mathematics and Computation, 195(1):299–308.
Jarboui, B., Ibrahim, S., Siarry, P., and Rebai, A. (2008b). A combinatorial particle swarm optimisation for solving permutation flowshop problems. Computers & Industrial Engineering, 54(3):526–538.
Karamolegkos, P. N., Patrikakis, C. Z., Doulamis, N. D., Vlacheas, P. T., and Nikolakopoulos, I. G. (2009). An evaluation study of clustering algorithms in the scope of user communities assessment. Computers & Mathematics with Applications, 58(8):1498 1519.
Kennedy, G., Coffrin, C., de Barba, P., and Corrin, L. (2015). Predicting success: how learners’ prior knowledge, skills and activities predict mooc performance. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, pages 136–140. ACM.
Lin, Y. T., Huang, Y. M., and Cheng, S.-C. (2010). An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization. Computers & Education, 55(4):1483–1493.
Linnenbrink-Garcia, L., Pugh, K. J., Koskey, K. L., and Stewart, V. C. (2012). Developing conceptual understanding of natural selection: The role of interest, efficacy, and basic prior knowledge. The Journal of Experimental Education, 80(1):45–68.
McAuley, A., Stewart, B., Siemens, G., and Cormier, D. (2010). The mooc model for digital practice, sshrc knowledge synthesis grant on the digital economy. Recuperado de [link].
Newman, D. R., Webb, B., and Cochrane, C. (1995). A content analysis method to measure critical thinking in face-to-face and computer supported group learning. Interpersonal Computing and Technology, 3(2):56–77.
Northouse, P. G. (2014). Introduction to leadership: Concepts and practice. Sage Publications.
Pedersen, M. E. H. and Chipperfield, A. J. (2010). Simplifying particle swarm optimization. Applied Soft Computing, 10(2):618–628.
Pierobom, J. L., Delgado, M. R., and Kaestner, C. A. (2012). Particle swarm optimization applied to the dynamic allocation problem. In Neural Networks (SBRN), 2012. Brazilian Symposium on, pages 184–189. IEEE.
Robinson, A. C., Kerski, J., Long, E. C., Luo, H., DiBiase, D., and Lee, A. (2015). Maps and the geospatial revolution: teaching a massive open online course (mooc) in geography. Journal of Geography in Higher Education, 39(1):65–82.
Rosendo, M. and Pozo, A. (2010). Applying a discrete particle swarm optimization algorithm to combinatorial problems. In Neural Networks (SBRN), 2010 Eleventh Brazilian Symposium on, pages 235–240.
Wang, S.-Q., Gong, L. H., and Yan, S. L. (2009). The allocation optimization of project human resource based on particle swarm optimization algorithm. In Services Science, Management and Engineering, 2009. SSME ’09. IITA International Conference on, pages 169–172.
Yang, S. J. (2006). Context aware ubiquitous learning environments for peer-to-peer collaborative learning. Journal of Educational Technology & Society, 9(1):188–201.
Publicado
11/11/2019
Como Citar
FERREIRA, Deller J.; ULMMANN, Matheus R. D..
Automatic Group Formation for Large Classes in Distance Education. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1936-1944.
DOI: https://doi.org/10.5753/cbie.sbie.2019.1936.
