Modelo de adaptação de conteúdo individualizada com base em estilos de aprendizagem
Resumo
O uso de sistemas inteligentes tem se ampliado notavelmente desde a introdução de novas técnicas de aprendizado de máquina, sendo isso reforçado a partir do surgimento das LLMs (\textit{Large Language Models}). Esse crescimento tem se observado também em ensino, que é uma área em que já há bastante tempo se introduziu os Sistemas Tutores Inteligentes. Neste contexto, uma aplicação interessante é a geração de conteúdos adaptados a estilos de aprendizagem, em que um material didático é produzido de forma customizada para cada categoria de aluno. Apresenta-se aqui uma ferramenta que usa técnicas de inteligência artificial para construção de conteúdos adaptados para o Inventório de Estilos de Aprendizagem criado por David Kolb. Essa ferramenta automatiza a produção de conteúdos específicos para cada estilo a partir de um texto base introduzido pelo professor. Os resultados obtidos mostram que o uso de LLMs permite a criação de textos específicos com facilidade, viabilizando ao professor produzir textos adaptados a cada perfil de aluno.
Palavras-chave:
Sistemas Tutores Inteligentes, Geração de Conteúdo, Geração de texto, Inteligência Artificial
Referências
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Coffield, F., Moseley, D., Hall, E., and Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. Learning & Skills Research Centre.
Corbett, A. T., Koedinger, K. R., and Anderson, J. R. (1997). Intelligent tutoring systems. In Handbook of human-computer interaction, pages 849–874. Elsevier.
Dunn, R. (1990). Rita dunn answers question on learning styles. Educational Leadership, 48(2):15–19.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7):674–681.
Firoozeh, N., Nazarenko, A., Alizon, F., and Daille, B. (2020). Keyword extraction: Issues and methods. Natural Language Engineering, 26(3):259–291
Gambhir, M. and Gupta, V. (2017). Recent automatic text summarization techniques: a survey. Artificial Intelligence Review, 47:1–66.
Hamalainen, W. and Vinni, M. (2006). Comparison of machine learning methods for intelligent tutoring systems. In International Conference on Intelligent Tutoring Systems, pages 525–534. Springer.
Han, J., Zhao, W., Jiang, Q., Oubibi, M., and Hu, X. (2019). Intelligent tutoring system trends 2006-2018: A literature review. In 2019 Eighth International Conference on Educational Innovation through Technology (EITT), pages 153–159. IEEE.
Idkhan, A. M. and Idris, M. M. (2021). Dimensions of students learning styles at the university with the kolb learning model. International Journal of Environment, Engineering & Education, 3(2):75–82.
Janjanam, P. and Reddy, C. P. (2019). Text summarization: An essential study. In 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), pages 1–6. IEEE.
Kolb, D. A. (1976). Management and the learning process. California management review, 18(3):21–31.
Kolb, D. A. (2007). The Kolb learning style inventory. Hay Resources Direct Boston, MA.
Korkmaz, C. and Correia, A.-P. (2019). A review of research on machine learning in educational technology. Educational Media International, 56(3):250–267.
Lavbic, D., Matek, T., and Zrnec, A. (2017). Recommender system for learning sql using hints. Interactive Learning Environments, 25(8):1048–1064.
Li, J., Tang, T., Zhao, W. X., Nie, J.-Y., and Wen, J.-R. (2022). Pretrained language models for text generation: A survey. arXiv preprint arXiv:2201.05273.
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., and Ghazi Saeedi, M. (2018). Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, pages 1–22.
Nancekivell, S. E., Shah, P., and Gelman, S. A. (2020). Maybe they’re born with it, or maybe it’s experience: Toward a deeper understanding of the learning style myth. Journal of Educational Psychology, 112(2):221–235.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8):9.
Santi, M., Manacero, A., Peronaglio, F. F., Lobato, R. S., Spolon, R., and Cavenaghi, M. A. (2022). Training transformers for question generation task in intelligent tutoring systems. In 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Sternberg, R. J. (1994). Allowing for thinking styles. Educational leadership, 52(3):36–40
Troussas, C., Krouska, A., and Virvou, M. (2019). Adaptive e-learning interactions using dynamic clustering of learners’ characteristics. In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pages 1–7.
Wong, L.-H. and Looi, C.-K. (2012). Swarm intelligence: new techniques for adaptive systems to provide learning support. Interactive Learning Environments, 20(1):19–40.
Zhang, Q., Guo, B., Wang, H., Liang, Y., Hao, S., and Yu, Z. (2019). Ai-powered text generation for harmonious human-machine interaction: Current state and future directions. In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Compu- ting, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pages 859–864. IEEE.
Zhiping, L., Yu, S., and Tianwei, X. (2011). A formal model of personalized recommendation systems in intelligent tutoring systems. In 2011 6th International Conference on Computer Science & Education (ICCSE), pages 1006–1009. IEEE.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
Coffield, F., Moseley, D., Hall, E., and Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. Learning & Skills Research Centre.
Corbett, A. T., Koedinger, K. R., and Anderson, J. R. (1997). Intelligent tutoring systems. In Handbook of human-computer interaction, pages 849–874. Elsevier.
Dunn, R. (1990). Rita dunn answers question on learning styles. Educational Leadership, 48(2):15–19.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7):674–681.
Firoozeh, N., Nazarenko, A., Alizon, F., and Daille, B. (2020). Keyword extraction: Issues and methods. Natural Language Engineering, 26(3):259–291
Gambhir, M. and Gupta, V. (2017). Recent automatic text summarization techniques: a survey. Artificial Intelligence Review, 47:1–66.
Hamalainen, W. and Vinni, M. (2006). Comparison of machine learning methods for intelligent tutoring systems. In International Conference on Intelligent Tutoring Systems, pages 525–534. Springer.
Han, J., Zhao, W., Jiang, Q., Oubibi, M., and Hu, X. (2019). Intelligent tutoring system trends 2006-2018: A literature review. In 2019 Eighth International Conference on Educational Innovation through Technology (EITT), pages 153–159. IEEE.
Idkhan, A. M. and Idris, M. M. (2021). Dimensions of students learning styles at the university with the kolb learning model. International Journal of Environment, Engineering & Education, 3(2):75–82.
Janjanam, P. and Reddy, C. P. (2019). Text summarization: An essential study. In 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), pages 1–6. IEEE.
Kolb, D. A. (1976). Management and the learning process. California management review, 18(3):21–31.
Kolb, D. A. (2007). The Kolb learning style inventory. Hay Resources Direct Boston, MA.
Korkmaz, C. and Correia, A.-P. (2019). A review of research on machine learning in educational technology. Educational Media International, 56(3):250–267.
Lavbic, D., Matek, T., and Zrnec, A. (2017). Recommender system for learning sql using hints. Interactive Learning Environments, 25(8):1048–1064.
Li, J., Tang, T., Zhao, W. X., Nie, J.-Y., and Wen, J.-R. (2022). Pretrained language models for text generation: A survey. arXiv preprint arXiv:2201.05273.
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., and Ghazi Saeedi, M. (2018). Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, pages 1–22.
Nancekivell, S. E., Shah, P., and Gelman, S. A. (2020). Maybe they’re born with it, or maybe it’s experience: Toward a deeper understanding of the learning style myth. Journal of Educational Psychology, 112(2):221–235.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8):9.
Santi, M., Manacero, A., Peronaglio, F. F., Lobato, R. S., Spolon, R., and Cavenaghi, M. A. (2022). Training transformers for question generation task in intelligent tutoring systems. In 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Sternberg, R. J. (1994). Allowing for thinking styles. Educational leadership, 52(3):36–40
Troussas, C., Krouska, A., and Virvou, M. (2019). Adaptive e-learning interactions using dynamic clustering of learners’ characteristics. In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pages 1–7.
Wong, L.-H. and Looi, C.-K. (2012). Swarm intelligence: new techniques for adaptive systems to provide learning support. Interactive Learning Environments, 20(1):19–40.
Zhang, Q., Guo, B., Wang, H., Liang, Y., Hao, S., and Yu, Z. (2019). Ai-powered text generation for harmonious human-machine interaction: Current state and future directions. In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Compu- ting, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pages 859–864. IEEE.
Zhiping, L., Yu, S., and Tianwei, X. (2011). A formal model of personalized recommendation systems in intelligent tutoring systems. In 2011 6th International Conference on Computer Science & Education (ICCSE), pages 1006–1009. IEEE.
Publicado
04/11/2024
Como Citar
PERONAGLIO, Fernanda F.; MANACERO JR., Aleardo; BALDASSIN, Alexandro J.; SANTOS, Matheus S. dos; LOBATO, Renata S.; SPOLON, Roberta; CAVENAGHI, Marcos A..
Modelo de adaptação de conteúdo individualizada com base em estilos de aprendizagem. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 35. , 2024, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
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p. 1957-1970.
DOI: https://doi.org/10.5753/sbie.2024.242720.
