Estratégia para adaptação de soluções de sequenciamento curricular adaptativo no Moodle
Resumo
A educação online exige abordagens personalizadas que respeitem as particularidades de cada estudante. Assim, o Sequenciamento Curricular Adaptativo (ACS) surge como uma solução promissora, mas sua aplicação ainda é limitada. Este estudo analisa criticamente abordagens de ACS, explorando sua adaptação em ambientes virtuais de aprendizagem, com foco no Moodle. Realizou-se um levantamento dos principais dados e métodos utilizados em ACS, mapeando esses elementos com recursos nativos do Moodle. Como resultado, propõe-se uma prova de conceito que demonstra a viabilidade de percursos adaptativos na plataforma. A pesquisa contribui para transformar modelos teóricos em soluções práticas, avançando para um ensino personalizado.Referências
Acampora, G., Gaeta, M., and Loia, V. (2011). Hierarchical optimization of personalized experiences for e-learning systems through evolutionary models. Neural Computing and Applications, 20(5):641–657.
Barrére, E., de Souza, J. F., Vitor, M. A., and de Almeida, M. A. (2020). Utilização de enriquecimento semântico para a recomendação automática de videoaulas no moodle. Revista Brasileira de Informática na Educação, 28:319–334.
de Castro Martins Ferreira Nogueira, J. V., Bernardino, H. S., de Souza, J. F., Gonçalves, L. B., and Soares, S. S. R. F. (2024). Exploring the solution space for adaptive curriculum sequencing: Study of a multi-objective approach. Internet of Things, 25:101052.
de Oliveira Costa Machado, M., Bravo, N. F. S., Martins, A. F., Bernardino, H. S., Barrere, E., and de Souza, J. F. (2020). Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature. Artificial Intelligence Review, 54(1):711–754.
Debnath, A., Sarkar, P., and Roy, S. (2013). An ant colony optimization based advice generation for curriculum sequencing under flexible learning system. In Proceedings of the International Conference on Advances in Computing, Electronics and Electrical Technology (CEET), pages 1–6, Kolkata, India. Association of Computer Electronics and Electrical Engineers (ACEEE).
do Carmo Marcheti Ferraz, A. P. and Belhot, R. V. (2010). Taxonomia de bloom: revisão teórica e apresentação das adequações do instrumento para definição de objetivos instrucionais. Gestão & Produção, 17(2):421–431.
Dwivedi, P., Kant, V., and Bharadwaj, K. K. (2018). Learning path recommendation based on modified variable length genetic algorithm. Education and Information Technologies, 23(2):819–836.
Ezzaim, A., Dahbi, A., Haidine, A., and Aqqal, A. (2024). The impact of implementing a moodle plug-in as an AI-based adaptive learning solution on learning effectiveness: Case of morocco. International Journal of Interactive Mobile Technologies (iJIM), 18(01):133–149. Acesso em: 26 maio 2025.
Gamage, S. H. P. W., Ayres, J. R., and Behrend, M. B. (2022). A systematic review on trends in using moodle for teaching and learning. International Journal of STEM Education, 9(1):9.
Hofmann, M. J., Remus, S., Biemann, C., Radach, R., and Kuchinke, L. (2022). Language models explain word reading times better than empirical predictability. Frontiers in Artificial Intelligence, 4:730570.
Huang, J., Ding, R., Wu, X., Chen, S., Zhang, J., Liu, L., and Zheng, Y. (2023). Werece: An unsupervised method for educational concept extraction based on word embedding refinement. Applied Sciences, 13(22):12307.
Legramante, D., Azevedo, A., and Azevedo, J. M. (2023). Integration of the technology acceptance model and the information systems success model in the analysis of moodle’s satisfaction and continuity of use. International Journal of Information and Learning Technology, 40(5):467–484.
Martins, A. F., Machado, M., Bernardino, H. S., and de Souza, J. F. (2021). A comparative analysis of metaheuristics applied to adaptive curriculum sequencing. In Soft Computing, volume 25, pages 11019–11034. Springer.
Menai, M. E. B., Alhunitah, H., and Al-Salman, H. (2018). Swarm intelligence to solve the curriculum sequencing problem. In Computer Applications in Engineering Education, volume 26, pages 1393–1404. Wiley.
Morze, N., Varchenko-Trotsenko, L., and Terletska, T. (2023). Stages of adaptive learning implementation by means of moodle lms. In Proceedings of the 2nd Myroslav I. Zhaldak Symposium on Advances in Educational Technology - AET, pages 476–487. INSTICC, SciTePress.
Morze, N., Varchenko-Trotsenko, L., Terletska, T., and Smyrnova-Trybulska, E. (2021). Implementation of adaptive learning at higher education institutions by means of moodle lms. Journal of Physics: Conference Series, 1840(1):012062.
Peng, X., Sun, X., and He, Z. (2022). A hybrid particle swarm optimizer for curriculum sequencing problem. In Discrete Dynamics in Nature and Society, volume 2022, pages 1–12. Hindawi.
Ramesh, V. M., Rao, N. J., and Ramanathan, C. (2015). Implementation of an intelligent tutoring system using moodle. In 2015 IEEE Frontiers in Education Conference (FIE), pages 1–9.
Sharma, R., Banati, H., and Bedi, P. (2012). Adaptive content sequencing for e-learning courses using ant colony optimization. In Deep, K., Nagar, A., Pant, M., and Bansal, J. C., editors, Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), pages 579–590, New Delhi. Springer India.
Silva, J., Barrere, E., and de Souza, J. (2022). Integração de soluções de sequenciamento curricular adaptativo ao moodle. In Anais do XXXIII Simpósio Brasileiro de Informática na Educação, pages 822–833, Porto Alegre, RS, Brasil. SBC.
Smith, J. and Doe, J. (2023). Revolutionizing education: Advanced machine learning techniques for precision recommendation of top-quality instructional materials. International Journal of Educational Technology, 15(3):123–145.
Barrére, E., de Souza, J. F., Vitor, M. A., and de Almeida, M. A. (2020). Utilização de enriquecimento semântico para a recomendação automática de videoaulas no moodle. Revista Brasileira de Informática na Educação, 28:319–334.
de Castro Martins Ferreira Nogueira, J. V., Bernardino, H. S., de Souza, J. F., Gonçalves, L. B., and Soares, S. S. R. F. (2024). Exploring the solution space for adaptive curriculum sequencing: Study of a multi-objective approach. Internet of Things, 25:101052.
de Oliveira Costa Machado, M., Bravo, N. F. S., Martins, A. F., Bernardino, H. S., Barrere, E., and de Souza, J. F. (2020). Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature. Artificial Intelligence Review, 54(1):711–754.
Debnath, A., Sarkar, P., and Roy, S. (2013). An ant colony optimization based advice generation for curriculum sequencing under flexible learning system. In Proceedings of the International Conference on Advances in Computing, Electronics and Electrical Technology (CEET), pages 1–6, Kolkata, India. Association of Computer Electronics and Electrical Engineers (ACEEE).
do Carmo Marcheti Ferraz, A. P. and Belhot, R. V. (2010). Taxonomia de bloom: revisão teórica e apresentação das adequações do instrumento para definição de objetivos instrucionais. Gestão & Produção, 17(2):421–431.
Dwivedi, P., Kant, V., and Bharadwaj, K. K. (2018). Learning path recommendation based on modified variable length genetic algorithm. Education and Information Technologies, 23(2):819–836.
Ezzaim, A., Dahbi, A., Haidine, A., and Aqqal, A. (2024). The impact of implementing a moodle plug-in as an AI-based adaptive learning solution on learning effectiveness: Case of morocco. International Journal of Interactive Mobile Technologies (iJIM), 18(01):133–149. Acesso em: 26 maio 2025.
Gamage, S. H. P. W., Ayres, J. R., and Behrend, M. B. (2022). A systematic review on trends in using moodle for teaching and learning. International Journal of STEM Education, 9(1):9.
Hofmann, M. J., Remus, S., Biemann, C., Radach, R., and Kuchinke, L. (2022). Language models explain word reading times better than empirical predictability. Frontiers in Artificial Intelligence, 4:730570.
Huang, J., Ding, R., Wu, X., Chen, S., Zhang, J., Liu, L., and Zheng, Y. (2023). Werece: An unsupervised method for educational concept extraction based on word embedding refinement. Applied Sciences, 13(22):12307.
Legramante, D., Azevedo, A., and Azevedo, J. M. (2023). Integration of the technology acceptance model and the information systems success model in the analysis of moodle’s satisfaction and continuity of use. International Journal of Information and Learning Technology, 40(5):467–484.
Martins, A. F., Machado, M., Bernardino, H. S., and de Souza, J. F. (2021). A comparative analysis of metaheuristics applied to adaptive curriculum sequencing. In Soft Computing, volume 25, pages 11019–11034. Springer.
Menai, M. E. B., Alhunitah, H., and Al-Salman, H. (2018). Swarm intelligence to solve the curriculum sequencing problem. In Computer Applications in Engineering Education, volume 26, pages 1393–1404. Wiley.
Morze, N., Varchenko-Trotsenko, L., and Terletska, T. (2023). Stages of adaptive learning implementation by means of moodle lms. In Proceedings of the 2nd Myroslav I. Zhaldak Symposium on Advances in Educational Technology - AET, pages 476–487. INSTICC, SciTePress.
Morze, N., Varchenko-Trotsenko, L., Terletska, T., and Smyrnova-Trybulska, E. (2021). Implementation of adaptive learning at higher education institutions by means of moodle lms. Journal of Physics: Conference Series, 1840(1):012062.
Peng, X., Sun, X., and He, Z. (2022). A hybrid particle swarm optimizer for curriculum sequencing problem. In Discrete Dynamics in Nature and Society, volume 2022, pages 1–12. Hindawi.
Ramesh, V. M., Rao, N. J., and Ramanathan, C. (2015). Implementation of an intelligent tutoring system using moodle. In 2015 IEEE Frontiers in Education Conference (FIE), pages 1–9.
Sharma, R., Banati, H., and Bedi, P. (2012). Adaptive content sequencing for e-learning courses using ant colony optimization. In Deep, K., Nagar, A., Pant, M., and Bansal, J. C., editors, Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), pages 579–590, New Delhi. Springer India.
Silva, J., Barrere, E., and de Souza, J. (2022). Integração de soluções de sequenciamento curricular adaptativo ao moodle. In Anais do XXXIII Simpósio Brasileiro de Informática na Educação, pages 822–833, Porto Alegre, RS, Brasil. SBC.
Smith, J. and Doe, J. (2023). Revolutionizing education: Advanced machine learning techniques for precision recommendation of top-quality instructional materials. International Journal of Educational Technology, 15(3):123–145.
Publicado
24/11/2025
Como Citar
NASCIMENTO, Luíza Machado Costa; BARRERE, Eduardo; VELOSO, Renê Rodrigues; SOUZA, Jairo Francisco de.
Estratégia para adaptação de soluções de sequenciamento curricular adaptativo no Moodle. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 36. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 439-452.
DOI: https://doi.org/10.5753/sbie.2025.12432.
