Desafios e Soluções no Ensino de ML para Estudantes em Situação de Vulnerabilidade Social
Resumo
Considerando a importância do Machine Learning atualmente, é essencial promover o ensino de ML desde cedo, visando à igualdade e incluindo também estudantes em situações de vulnerabilidade social. Este relato de experiência analisa e discute a aplicação do curso ML4ALL! a 158 estudantes do ensino fundamental e médio em situação de vulnerabilidade social. Os resultados mostram que os estudantes aprenderam a entender conceitos de ML e a desenvolver seu primeiro modelo de ML. Apesar dos desafios nesse contexto, os resultados sugerem que as barreiras podem ser superadas por meio da criação de parcerias entre instituições sociais e universidades, do apoio individualizado e de abordagens pedagógicas adaptadas.
Referências
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Amershi, S., et al. (2019). Software engineering for machine learning: A case study. Proc. of the Int. Conference on Software Engineering: Software Engineering in Practice. Montreal, QC, Canada.
Basili, V. R., Caldiera, G., & Rombach, H. D. (1994). Goal question metric paradigm. In Encyclopedia of Software Engineering. Wiley.
Camada M. Y. e Durães G. M., (2020), Ensino da Inteligência Artificial na Educação Básica: um novo horizonte para as pesquisas brasileiras. Anais do XXXI Simpósio Brasileiro de Informática na Educação, Natal, RN, Brasil.
Caruso A. L. M. e Cavalheiro S. A. da C., (2021), Integração entre Pensamento Computacional e Inteligência Artificial: uma Revisão Sistemática de Literatura. Anais do XXXII Simpósio Brasileiro de Informática na Educação. Online
CSTA. (2017). K–12 Computer Science Framework. http://www.k12cs.org.
Google. (2022). Google Teachable Machine. https://teachablemachine.withgoogle.com/
Gresse von Wangenheim, C., Marques, L. S., Rauber, M. F., & Hauck, J. C. R. (2022). A Proposal for Performance-based Assessment of the Learning of Machine Learning Concepts and Practices in K-12. Informatics in Education, 21(3).
Gresse von Wangenheim, C., Marques, L. S., & Hauck, J. C. R. (2020). Machine Learning for All – Introducing Machine Learning in K-12. SocArXiv.
IBM. (2023). IBM SkillsBuild. https://skillsbuild.org/.
Lee, I. et al. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1).
Li, Y. (2022). Research and application of deep learning in image recognition. Proc. of the Int. Conference on Power, Electronics and Computer Applications . Shenyang, China.
Martins, R. M., Gresse von Wangenheim, C., Rauber, M. F., & Hauck, J. C. (2023). Machine Learning for All!—Introducing Machine Learning in Middle and High School. International Journal of Artificial Intelligence in Education. Online.
Martins, R. M. & Gresse von Wangenheim, C., (2023). Teaching Computing to Middle and High School Students from a Low Socio-Economic Status Background: A Systematic Literature Review. Informatics in Education. Online.
MEC. (2017). Base Nacional Comum Curricular. http://basenacionalcomum.mec.gov.br.
OECD PISA. (2018). PISA 2018 Results Where All Students Can Succeed: Country Note Brazil.
Parker, M. C., & Guzdial, M. (2015). A critical research synthesis of privilege in computing education. Proc. of Research in Equity and Sustained Participation in Engineering, Computing, and Technology, Charlotte, NC, USA.
Rauber, M. F. et al. (2023). Reliability and Validity of an Automated Model for Assessing the Learning of Machine Learning in Middle and High School. Informatics in Education. Submetido.
SBC. (2018). Diretrizes para ensino de Computação na Educação Básica. Sociedade Brasileira de Computação. [link].
The Coding School. (2023). https://the-cs.org/.
Tissenbaum, M., Sheldon, J., & Abelson, H. (2019). From Computational Thinking to Computational Action. Communications of the ACM, 62(3).
Touretzky, D. S., Gardner-McCune, C., & Seehorn, D. (2022). Machine learning and the five big ideas in AI. International Journal of Artificial Intelligence in Education. Springer Nature.
UNESCO. (2022). K-12 AI curricula – A mapping of government-endorsed AI curricula. Paris.
UNRISDSP. (2015). According to Social Protection and Human Rights. United Nations Research Institute for Social Development.
World Economic Forum. (2020). The Future of Jobs Report.
Yin, R. K. (2017). Case study research and applications: Design and methods. SAGE Publications.
Amershi, S., et al. (2019). Software engineering for machine learning: A case study. Proc. of the Int. Conference on Software Engineering: Software Engineering in Practice. Montreal, QC, Canada.
Basili, V. R., Caldiera, G., & Rombach, H. D. (1994). Goal question metric paradigm. In Encyclopedia of Software Engineering. Wiley.
Camada M. Y. e Durães G. M., (2020), Ensino da Inteligência Artificial na Educação Básica: um novo horizonte para as pesquisas brasileiras. Anais do XXXI Simpósio Brasileiro de Informática na Educação, Natal, RN, Brasil.
Caruso A. L. M. e Cavalheiro S. A. da C., (2021), Integração entre Pensamento Computacional e Inteligência Artificial: uma Revisão Sistemática de Literatura. Anais do XXXII Simpósio Brasileiro de Informática na Educação. Online
CSTA. (2017). K–12 Computer Science Framework. http://www.k12cs.org.
Google. (2022). Google Teachable Machine. https://teachablemachine.withgoogle.com/
Gresse von Wangenheim, C., Marques, L. S., Rauber, M. F., & Hauck, J. C. R. (2022). A Proposal for Performance-based Assessment of the Learning of Machine Learning Concepts and Practices in K-12. Informatics in Education, 21(3).
Gresse von Wangenheim, C., Marques, L. S., & Hauck, J. C. R. (2020). Machine Learning for All – Introducing Machine Learning in K-12. SocArXiv.
IBM. (2023). IBM SkillsBuild. https://skillsbuild.org/.
Lee, I. et al. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1).
Li, Y. (2022). Research and application of deep learning in image recognition. Proc. of the Int. Conference on Power, Electronics and Computer Applications . Shenyang, China.
Martins, R. M., Gresse von Wangenheim, C., Rauber, M. F., & Hauck, J. C. (2023). Machine Learning for All!—Introducing Machine Learning in Middle and High School. International Journal of Artificial Intelligence in Education. Online.
Martins, R. M. & Gresse von Wangenheim, C., (2023). Teaching Computing to Middle and High School Students from a Low Socio-Economic Status Background: A Systematic Literature Review. Informatics in Education. Online.
MEC. (2017). Base Nacional Comum Curricular. http://basenacionalcomum.mec.gov.br.
OECD PISA. (2018). PISA 2018 Results Where All Students Can Succeed: Country Note Brazil.
Parker, M. C., & Guzdial, M. (2015). A critical research synthesis of privilege in computing education. Proc. of Research in Equity and Sustained Participation in Engineering, Computing, and Technology, Charlotte, NC, USA.
Rauber, M. F. et al. (2023). Reliability and Validity of an Automated Model for Assessing the Learning of Machine Learning in Middle and High School. Informatics in Education. Submetido.
SBC. (2018). Diretrizes para ensino de Computação na Educação Básica. Sociedade Brasileira de Computação. [link].
The Coding School. (2023). https://the-cs.org/.
Tissenbaum, M., Sheldon, J., & Abelson, H. (2019). From Computational Thinking to Computational Action. Communications of the ACM, 62(3).
Touretzky, D. S., Gardner-McCune, C., & Seehorn, D. (2022). Machine learning and the five big ideas in AI. International Journal of Artificial Intelligence in Education. Springer Nature.
UNESCO. (2022). K-12 AI curricula – A mapping of government-endorsed AI curricula. Paris.
UNRISDSP. (2015). According to Social Protection and Human Rights. United Nations Research Institute for Social Development.
World Economic Forum. (2020). The Future of Jobs Report.
Yin, R. K. (2017). Case study research and applications: Design and methods. SAGE Publications.
Publicado
06/11/2023
Como Citar
MARTINS, Ramon Mayor; WANGENHEIM, Christiane Gresse von; RAUBER, Marcelo F.; HAUCK, Jean C. R.; SILVESTRE, Melissa Figueiredo.
Desafios e Soluções no Ensino de ML para Estudantes em Situação de Vulnerabilidade Social. In: WORKSHOP DE INFORMÁTICA NA ESCOLA (WIE), 29. , 2023, Passo Fundo/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 1-12.
DOI: https://doi.org/10.5753/wie.2023.232864.
