Uma Abordagem Evolutiva para o Problema de Sequenciamento Curricular Adaptativo
Resumo
O Sequenciamento Curricular Adaptativo (SCA) ainda é um desafio na área de Aprendizagem Adaptativa. O SCA é um problema NP-Difícil, principalmente por considerar as restrições relacionadas ao perfil do aluno e dos materiais didáticos na seleção de uma sequência em repositórios onde várias poderiam ser escolhidas. Portanto, isso tem estimulado pesquisadores a utilizarem abordagens evolutivas na busca por soluções satisfatórias. Este trabalho utiliza o Algoritmo Presa-Predador, o qual se mostrou adequado para o problema, de acordo com experimentos realizados em laboratório, embora seja uma solução ainda não explorada na literatura de Aprendizagem Adaptativa.
Palavras-chave:
Sequenciamento Curricular Adaptativo, Aprendizagem Adaptativa, Algoritmo Presa-Predador, Abordagens Evolutivas, NP-Difícil
Referências
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Brusilovsky, P. (2003). Adaptive and intelligent technologies for web-based education. International Journal of Artificial Intelligence in Education, 13(4):159–172.
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Dembo, M. H. and Howard, K. (2007). Advice about the use of learning styles: A major myth in education. Journal of college reading and learning, 37(2):101–109.
do Nascimento, P., Barreto, R., Primo, T., Gusmão, T., and Oliveira, E. (2017). Recomendação de objetos de aprendizagem baseada em modelos de estilos de aprendizagem: Uma revisão sistemática da literatura. In Simpósio Brasileiro de Informática na Educação, volume 28, page 213.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7):674–681.
Hafidi, M. and Bensebaa, T. (2015). Architecture for an adaptive and intelligent tutoring system that considers the learner’s multiple intelligences. International Journal of Distance Education Technologies, 13(1):1–21.
Hnida, M., Idrissi, M. K., and Bennani, S. (2016). Adaptive teaching learning sequence based on instructional design and evolutionary computation. In International Conference on Information Technology Based Higher Education and Training, pages 1–6.
Husmann, P. R. and O’Loughlin, V. D. (2018). Another nail in the coffin for learning styles? Disparities among undergraduate anatomy students’ study strategies, class performance, and reported VARK learning styles. Anatomical Sciences Education.
Kardan, A. A., Aziz, M., and Shahpasand, M. (2015). Adaptive systems: a content analysis on technical side for e-learning environments. Artificial Intelligence Review, 44(3):365–391.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106:166–171.
Li, J.-W., Chang, Y.-C., Chu, C.-P., and Tsai, C.-C. (2012). A self-adjusting e-course generation process for personalized learning. Expert Systems with Applications, 39(3):3223–3232.
Machado, M., Souza, J., and Barrére, E. (2017). Geração de sequências curriculares adaptativas baseada em computação evolucionária: Estado da arte e tendências. volume 28, page 1137.
Muhammad, A., Zhou, Q., Beydoun, G., Xu, D., and Shen, J. (2016). Learning path adaptation in online learning systems. In International Conference on Computer Supported Cooperative Work in Design, pages 421–426. IEEE.
Nwana, H. S. (1990). Intelligent tutoring systems: an overview. Artificial Intelligence Review, 4(4):251–277.
Phobun, P. and Vicheanpanya, J. (2010). Adaptive intelligent tutoring systems for e-learning systems. Procedia-Social and Behavioral Sciences, 2(2):4064–4069.
Premlatha, K. and Geetha, T. (2015). Learning content design and learner adaptation for adaptive e-learning environment: a survey. Artificial Intelligence Review, 44(4):443–465.
Pushpa, M. (2012). ACO in e-learning: Towards an adaptive learning path. International Journal on Computer Science and Engineering, 4(3):458.
Silva, R. C., Direne, A. I., Marczal, D., Borille, A. C., Guimarães, P. R. B., da Silva Cabral, A., and Camargo, B. F. (2018). Adaptability of learning objects using calibration and adaptive sequencing of exercises. Brazilian Journal of Computers in Education, 26(01):70.
Tilahun, S. L. and Ong, H. C. (2015). Prey-predator algorithm: A new metaheuristic algorithm for optimization problems. International Journal of Information Technology & Decision Making, 14(06):1331–1352.
Van Merriënboer, J. J. and Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. Educational Technology Research and Development, 53(3):5–13.
Xie, H., Zou, D., Wang, F. L., Wong, T.-L., Rao, Y., and Wang, S. H. (2017). Discover learning path for group users: A profile-based approach. Neurocomputing, 254:59–70.
Al-Muhaideb, S. and Menai, M. E. B. (2011). Evolutionary computation approaches to the curriculum sequencing problem. Natural Computing, 10(2):891–920.
Brusilovsky, P. (2003). Adaptive and intelligent technologies for web-based education. International Journal of Artificial Intelligence in Education, 13(4):159–172.
Butcher, N. (2015). A basic guide to open educational resources (OER). Commonwealth of Learning (COL).
Dembo, M. H. and Howard, K. (2007). Advice about the use of learning styles: A major myth in education. Journal of college reading and learning, 37(2):101–109.
do Nascimento, P., Barreto, R., Primo, T., Gusmão, T., and Oliveira, E. (2017). Recomendação de objetos de aprendizagem baseada em modelos de estilos de aprendizagem: Uma revisão sistemática da literatura. In Simpósio Brasileiro de Informática na Educação, volume 28, page 213.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7):674–681.
Hafidi, M. and Bensebaa, T. (2015). Architecture for an adaptive and intelligent tutoring system that considers the learner’s multiple intelligences. International Journal of Distance Education Technologies, 13(1):1–21.
Hnida, M., Idrissi, M. K., and Bennani, S. (2016). Adaptive teaching learning sequence based on instructional design and evolutionary computation. In International Conference on Information Technology Based Higher Education and Training, pages 1–6.
Husmann, P. R. and O’Loughlin, V. D. (2018). Another nail in the coffin for learning styles? Disparities among undergraduate anatomy students’ study strategies, class performance, and reported VARK learning styles. Anatomical Sciences Education.
Kardan, A. A., Aziz, M., and Shahpasand, M. (2015). Adaptive systems: a content analysis on technical side for e-learning environments. Artificial Intelligence Review, 44(3):365–391.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106:166–171.
Li, J.-W., Chang, Y.-C., Chu, C.-P., and Tsai, C.-C. (2012). A self-adjusting e-course generation process for personalized learning. Expert Systems with Applications, 39(3):3223–3232.
Machado, M., Souza, J., and Barrére, E. (2017). Geração de sequências curriculares adaptativas baseada em computação evolucionária: Estado da arte e tendências. volume 28, page 1137.
Muhammad, A., Zhou, Q., Beydoun, G., Xu, D., and Shen, J. (2016). Learning path adaptation in online learning systems. In International Conference on Computer Supported Cooperative Work in Design, pages 421–426. IEEE.
Nwana, H. S. (1990). Intelligent tutoring systems: an overview. Artificial Intelligence Review, 4(4):251–277.
Phobun, P. and Vicheanpanya, J. (2010). Adaptive intelligent tutoring systems for e-learning systems. Procedia-Social and Behavioral Sciences, 2(2):4064–4069.
Premlatha, K. and Geetha, T. (2015). Learning content design and learner adaptation for adaptive e-learning environment: a survey. Artificial Intelligence Review, 44(4):443–465.
Pushpa, M. (2012). ACO in e-learning: Towards an adaptive learning path. International Journal on Computer Science and Engineering, 4(3):458.
Silva, R. C., Direne, A. I., Marczal, D., Borille, A. C., Guimarães, P. R. B., da Silva Cabral, A., and Camargo, B. F. (2018). Adaptability of learning objects using calibration and adaptive sequencing of exercises. Brazilian Journal of Computers in Education, 26(01):70.
Tilahun, S. L. and Ong, H. C. (2015). Prey-predator algorithm: A new metaheuristic algorithm for optimization problems. International Journal of Information Technology & Decision Making, 14(06):1331–1352.
Van Merriënboer, J. J. and Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. Educational Technology Research and Development, 53(3):5–13.
Xie, H., Zou, D., Wang, F. L., Wong, T.-L., Rao, Y., and Wang, S. H. (2017). Discover learning path for group users: A profile-based approach. Neurocomputing, 254:59–70.
Publicado
29/10/2018
Como Citar
MACHADO, Marcelo O. C.; BARRÉRE, Eduardo; SOUZA, Jairo F..
Uma Abordagem Evolutiva para o Problema de Sequenciamento Curricular Adaptativo. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 29. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 1243-1252.
DOI: https://doi.org/10.5753/cbie.sbie.2018.1243.
