Sequenciamento de Ações Pedagógicas por Algoritmo Genético Utilizando Taxonomia de Bloom e ASSIST
Resumo
Este trabalho propõe o sequenciamento automático e personalizado de ações pedagógicas, sob a perspectiva do planejamento automatizado baseado em algoritmo genético. As ações foram modeladas a partir da estrutura apresentada pela análise bidimensional da Taxonomia de Bloom (TB). A personalização das ações ocorre em consonância com o perfil cognitivo do estudante, pautado nas características dadas pelo Approaches and Study Skills Inventory for Students (ASSIST). Para tal, neste trabalho, foi mapeada uma relação entre TB e o ASSIST. Os resultados mostram que é possível realizar o sequenciamento de ações pedagógicas personalizadas tendo como base o perfil cognitivo do estudante, a partir da relação proposta.
Palavras-chave:
Planejamento Automatizado, Perfil Cognitivo do Estudante, Problema de Otimização, Algoritmo Evolutivo
Referências
Agbonifo, O. C. and Olanrewaju, A. O. (2018). Genetic algorithm-based curriculum sequencing model for personalized e-learning system [j]. International Journal of Education and Management Engineering, 5(8):27–35.
Ariyaratne, M. and Fernando, T. (2014). A comparative study on nature inspired algorithms with firefly algorithm. International Journal of Engineering and Technology,4(10):611–617.
Brown, S., White, S., Wakeling, L., and Naiker, M. (2015). Approaches and study skills inventory for students (ASSIST) in an introductory course in chemistry. Journal of University Teaching & Learning Practice.
Costa, N., Júnior, C. P., and Fernandes, M. (2019a). Recomendação de ações pedagógicas utilizando planejamento automático e taxonomia digital de Bloom. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 30, page 1531.
Costa, N., Pereira Junior, C., Araújo, R., and Fernandes, M. (2019b). Application of AI planning in the context of e-learning. In International Conference on Advanced Learning Technologies (ICALT), page 57.
de Miranda, P. B., Ferreira, R., Castro, M. S., Neto, G. F., Souza, S. J., Santos, L. A., and Silva, L. L. (2019). Uma abordagem multiobjetivo para recomendação de caminhos de aprendizagem para grupo de usuários. Revista Brasileira de Informática na Educação, 27(3).
Engelbrecht, A. P. (2007). Computational intelligence: an introduction. John Wiley &Sons.
Entwistle, N., McCune, V., and Tait, H. (1997). The approaches and study skills inventory for students (assist). Edinburgh: Centre for Research on Learning and Instruction, University of Edinburgh.
Entwistle, N. and Tait, H. (2013). Approaches and study skills inventory for students(assist) (incorporating the revised approaches to studying inventory – RASI). Edinburgh: Centre for Research on Learning and Instruction, University of Edinburgh.
Garrido, A., Morales, L., and Serina, I. (2016). On the use of case-based planning fore-learning personalization. Expert Systems with Applications, 60:1–15.
Hssina, B. and Erritali, M. (2019). A personalized pedagogical objectives based on a genetic algorithm in an adaptive learning system. Procedia Computer Science, 151:1152–1157.
Krathwohl, D. R. (2002). A revision of bloom’s taxonomy: An overview. Theory Into Practice, 41(4):212–218.
Limongelli, C. and Sciarrone, F. (2014). Fuzzy student modeling for personalization of e-learning courses. In Zaphiris, P. and Ioannou, A., editors, Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences, pages 292–301, Cham. Springer International Publishing.
Lin, Y.-S., Chang, Y.-C., and Chu, C.-P. (2016). An innovative approach to scheme learning map considering tradeoff multiple objectives. Journal of Educational Technology& Society, 19(1):142–157.
Machado, M., Barrére, E., and Souza, J. (2018). Uma abordagem evolutiva para o problema de sequenciamento curricular adaptativo. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 29,page 1243.
Pireva, K. and Kefalas, P. (2018). A recommender system based on hierarchical clustering for cloud e-learning. Intelligent Distributed Computing XI, 53:235 – 245.
Rastegarmoghadam, M. and Ziarati, K. (2017). Improved modeling of intelligent tutoring systems using ant colony optimization. Education and Information Technologies,22(3):1067–1087.
Shang, H. (2019). Cultural interpretation of deep approach to learning: an empirical analysis in a chinese university. In Cross-Cultural Business Conference 2019, page207.
Vanitha, V., Krishnan, P., and Elakkiya, R. (2019). Collaborative optimization algorithm for learning path construction in e-learning. Computers & Electrical Engineering, 77:325–338.
Ariyaratne, M. and Fernando, T. (2014). A comparative study on nature inspired algorithms with firefly algorithm. International Journal of Engineering and Technology,4(10):611–617.
Brown, S., White, S., Wakeling, L., and Naiker, M. (2015). Approaches and study skills inventory for students (ASSIST) in an introductory course in chemistry. Journal of University Teaching & Learning Practice.
Costa, N., Júnior, C. P., and Fernandes, M. (2019a). Recomendação de ações pedagógicas utilizando planejamento automático e taxonomia digital de Bloom. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 30, page 1531.
Costa, N., Pereira Junior, C., Araújo, R., and Fernandes, M. (2019b). Application of AI planning in the context of e-learning. In International Conference on Advanced Learning Technologies (ICALT), page 57.
de Miranda, P. B., Ferreira, R., Castro, M. S., Neto, G. F., Souza, S. J., Santos, L. A., and Silva, L. L. (2019). Uma abordagem multiobjetivo para recomendação de caminhos de aprendizagem para grupo de usuários. Revista Brasileira de Informática na Educação, 27(3).
Engelbrecht, A. P. (2007). Computational intelligence: an introduction. John Wiley &Sons.
Entwistle, N., McCune, V., and Tait, H. (1997). The approaches and study skills inventory for students (assist). Edinburgh: Centre for Research on Learning and Instruction, University of Edinburgh.
Entwistle, N. and Tait, H. (2013). Approaches and study skills inventory for students(assist) (incorporating the revised approaches to studying inventory – RASI). Edinburgh: Centre for Research on Learning and Instruction, University of Edinburgh.
Garrido, A., Morales, L., and Serina, I. (2016). On the use of case-based planning fore-learning personalization. Expert Systems with Applications, 60:1–15.
Hssina, B. and Erritali, M. (2019). A personalized pedagogical objectives based on a genetic algorithm in an adaptive learning system. Procedia Computer Science, 151:1152–1157.
Krathwohl, D. R. (2002). A revision of bloom’s taxonomy: An overview. Theory Into Practice, 41(4):212–218.
Limongelli, C. and Sciarrone, F. (2014). Fuzzy student modeling for personalization of e-learning courses. In Zaphiris, P. and Ioannou, A., editors, Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences, pages 292–301, Cham. Springer International Publishing.
Lin, Y.-S., Chang, Y.-C., and Chu, C.-P. (2016). An innovative approach to scheme learning map considering tradeoff multiple objectives. Journal of Educational Technology& Society, 19(1):142–157.
Machado, M., Barrére, E., and Souza, J. (2018). Uma abordagem evolutiva para o problema de sequenciamento curricular adaptativo. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), volume 29,page 1243.
Pireva, K. and Kefalas, P. (2018). A recommender system based on hierarchical clustering for cloud e-learning. Intelligent Distributed Computing XI, 53:235 – 245.
Rastegarmoghadam, M. and Ziarati, K. (2017). Improved modeling of intelligent tutoring systems using ant colony optimization. Education and Information Technologies,22(3):1067–1087.
Shang, H. (2019). Cultural interpretation of deep approach to learning: an empirical analysis in a chinese university. In Cross-Cultural Business Conference 2019, page207.
Vanitha, V., Krishnan, P., and Elakkiya, R. (2019). Collaborative optimization algorithm for learning path construction in e-learning. Computers & Electrical Engineering, 77:325–338.
Publicado
24/11/2020
Como Citar
COSTA, Newarney Torrezão da; FERNANDES, Marcia Aparecida.
Sequenciamento de Ações Pedagógicas por Algoritmo Genético Utilizando Taxonomia de Bloom e ASSIST. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 1273-1282.
DOI: https://doi.org/10.5753/cbie.sbie.2020.1273.
