Aprendizado de Máquina com TinyML na Educação Básica: Um Relato de Experiência
Resumo
Em nossa sociedade moderna, as oportunidades no mercado de trabalho destacam cada vez mais, qualificações e habilidades com base no domínio das novas tecnologias. A inteligência artificial e o aprendizado de máquina são algumas destas tecnologias que permeiam a nossa vida atual em diversas aplicações, exigindo um entendimento maior por parte de quem pretende propor soluções que facilitem a execução de tarefas cotidianas. A formação escolar deve preparar alunos para esta realidade. Este trabalho relata a experiência de introdução ao aprendizado de máquina na educação básica com uma proposta de iniciação utilizando pequenos dispositivos de hardware e programação.Referências
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Huang, C.-J., Wu, T., Lu, J.-T., Lin, B., Chang, D., Wang, P., Wang, M.-C., Lee, P., and Wang, W. (2021). Developing a medical artificial intelligence course for high school students. In International Forum on Medical Imaging in Asia 2021, volume 11792, pages 103–108. SPIE.
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., and Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. In Proc. of IEEE Frontiers in Education Conference.
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Priya, S., Bhadra, S., Chimalakonda, S., and Venigalla, A. S. M. (2022). Ml-quest: a game for introducing machine learning concepts to k-12 students. Interactive Learning Environments, pages 1–16.
Rizvi, S., Waite, J., and Sentance, S. (2023). Artificial intelligence teaching and learning in k-12 from 2019 to 2022: A systematic literature review. Computers and Education: Artificial Intelligence, page 100145.
Rodríguez-García, J. D., Moreno-León, J., Román-González, M., and Robles, G. (2020). Introducing artificial intelligence fundamentals with learningml: Artificial intelligence made easy. In Eighth international conference on technological ecosystems for enhancing multiculturality, pages 18–20.
Santana, O. A., de Sousa, B. A., do Monte, S. R. S., de Freitas Lima, M. L., and e Silva, C. F. (2020). Deep learning practice for high school student engagement in stem careers. In 2020 IEEE Global Engineering Education Conference (EDUCON), pages 164–169. IEEE.
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Huang, C.-J., Wu, T., Lu, J.-T., Lin, B., Chang, D., Wang, P., Wang, M.-C., Lee, P., and Wang, W. (2021). Developing a medical artificial intelligence course for high school students. In International Forum on Medical Imaging in Asia 2021, volume 11792, pages 103–108. SPIE.
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., and Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. In Proc. of IEEE Frontiers in Education Conference.
Kim, S., Jang, Y., Choi, S., Kim, W., Jung, H., Kim, S., and Kim, H. (2021). Analyzing teacher competency with tpack for k-12 ai education. KI-Künstliche Intelligenz, 35(2):139–151.
Marques, L. S., von Wangenheim, C. G., and Hauck, J. C. R. (2020). Ensino de machine learning na educação básica: um mapeamento sistemático do estado da arte. In Anais do XXXI Simpósio Brasileiro de Informática na Educação, pages 21–30. SBC.
Mioto, F., Petri, G., von Wangenheim, C. G., Borgatto, A. F., and Pacheco, L. H. M. (2019). bases21-um modelo para a autoavaliaçao de habilidades do século xxi no contexto do ensino de computaçao na educaçao básica. Revista Brasileira de Informática na Educação, 27(01):26.
Priya, S., Bhadra, S., Chimalakonda, S., and Venigalla, A. S. M. (2022). Ml-quest: a game for introducing machine learning concepts to k-12 students. Interactive Learning Environments, pages 1–16.
Rizvi, S., Waite, J., and Sentance, S. (2023). Artificial intelligence teaching and learning in k-12 from 2019 to 2022: A systematic literature review. Computers and Education: Artificial Intelligence, page 100145.
Rodríguez-García, J. D., Moreno-León, J., Román-González, M., and Robles, G. (2020). Introducing artificial intelligence fundamentals with learningml: Artificial intelligence made easy. In Eighth international conference on technological ecosystems for enhancing multiculturality, pages 18–20.
Santana, O. A., de Sousa, B. A., do Monte, S. R. S., de Freitas Lima, M. L., and e Silva, C. F. (2020). Deep learning practice for high school student engagement in stem careers. In 2020 IEEE Global Engineering Education Conference (EDUCON), pages 164–169. IEEE.
Seehorn, D., Carey, S., Fuschetto, B., Lee, I., Moix, D., O’Grady-Cunniff, D., Owens, B. B., Stephenson, C., and Verno, A. (2011). CSTA K–12 Computer Science Standards: Revised 2011. ACM.
Select Committee on Artificial Intelligence (2018). AI in the UK: ready, willing and able? Technical report.
Touretzky, D., Gardner-McCune, C., Martin, F., and Seehorn, D. (2019). Envisioning ai for k-12: What should every child know about ai? In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 9795–9799.
Zhu, K. (2019). An educational approach to machine learning with mobile applications. PhD thesis, Massachusetts Institute of Technology.
Publicado
06/11/2023
Como Citar
SAMPAIO, Algeir P.; FARIAS, Paulo C. M. A.; BITTENCOURT, Roberto A..
Aprendizado de Máquina com TinyML na Educação Básica: Um Relato de Experiência. In: WORKSHOP DE INFORMÁTICA NA ESCOLA (WIE), 29. , 2023, Passo Fundo/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 108-118.
DOI: https://doi.org/10.5753/wie.2023.234246.
