Recomendação Pedagógica Personalizada a partir do Sequenciamento de Ações Baseadas na Taxonomia de Bloom e no Perfil RASI usando Planejamento em Inteligência Artificial

• Newarney Torrezão da Costa IF Goiano / UFU
• Márcia Aparecida Fernandes UFU

DOI: https://doi.org/10.5753/cbie_estendido.2023.234579

Abstract

The sequencing and recommendation of personalized pedagogical actions are relevant aspects of virtual learning environments (VLEs) in an attempt to promote and make effective computer-mediated teaching. This paper proposes a multiobjective genetic algorithm to sequence pedagogical actions based on Bloom’s Taxonomy and recommend digital activities. The sequencing customization is according to the student’s profile provided by the Revised Approaches to Studying Inventory (RASI). Experiments and statistical analysis showed promising results and pointed to the proposal’s viability with the potential to compose VLEs.

References

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.

Bloom, B., Engelhart, M., Furst, E., Hill, W., and Krathwohl, D. (1984). Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain. David McKay, 1st edition.

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. https://doi.org/10.53761/1.12.3.6.

Churches, A. (2010). Bloom’s digital taxonomy.

Costa, N. and Fernandes, M. (2020). Sequenciamento de ações pedagógicas por algoritmo genético utilizando taxonomia de bloom e assist. In Anais do XXXI Simpósio Brasileiro de Informática na Educação, pages 1273–1282, Porto Alegre, RS, Brasil. SBC. https://doi.org/10.5753/cbie.sbie.2020.1273.

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. https://doi.org/10.5753/cbie.sbie.2019.1531.

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. https://doi.org/10.1109/ICALT.2019.00021.

Costa, N. T., de Almeida, D. J., Oliveira, G. P., and Fernandes, M. A. (2022a). Customized pedagogical recommendation using automated planning for sequencing based on bloom’s taxonomy. International Journal of Distance Education Technologies (IJDET), 20(1):1–19. https://doi.org/10.4018/ijdet.296700.

Costa, N. T. et al. (2022b). Sequenciamento e recomendação de ações pedagógicas baseados na taxonomia de bloom e no perfil rasi usando planejamento automatizado por algoritmo genético. Universidade Federal de Uberlândia – Programa de Pós-Graduação em Ciência da Computação. http://doi.org/10.14393/ufu.te.2022.535.

Costa, N. T. and Fernandes, M. A. (2021). Sequenciamento de ações pedagógicas baseadas na taxonomia de bloom usando planejamento automatizado apoiado por algoritmo genético. Revista Brasileira de Informática na Educação, 29:485–501.

Duff, A. (2004). The revised approaches to studying inventory (rasi) and its use in management education. Active learning in higher education, 5(1):56–72. https://doi.org/10.1177/1469787404040461.

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.

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. https://doi.org/10.1016/j.procs.2019.04.164.

Krathwohl, D. R. (2002). A revision of bloom’s taxonomy: An overview. Theory Into Practice, 41(4):212–218. https://doi.org/10.1207/s15430421tip4104_2.

Moro, F. F., Tarouco, L. M. R., and Vicari, R. M. (2021). Proposta de arquitetura baseada em agentes inteligentes integrados em ambientes e-learning. Revista Educar Mais, 5(2):249–260. https://doi.org/10.15536/reducarmais.5.2021.2163.

Peng, H., Ma, S., and Spector, J. M. (2019). Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1):1–14. https://doi.org/10.1186/s40561-019-0089-y.

Russel, S., Norvig, P., et al. (2013). Artificial intelligence: a modern approach. Pearson Education Limited London.

Shang, H. (2019). Cultural interpretation of deep approach to learning: an empirical analysis in a chinese university. In Cross-Cultural Business Conference 2019, page 207.