ML4Teens - Introduzindo Machine Learning no Ensino Médio
Resumo
Considerando a relevância do Machine Learning (ML) atualmente, tornou-se importante a popularização de conceitos de ML já no Ensino Médio. No entanto, existem poucas iniciativas com esse foco para esse estágio educacional. Portanto, este artigo apresenta um curso online desenvolvido para ensinar a compreensão básica e a aplicação de ML e redes neurais no Ensino Médio. Uma avaliação preliminar indica que o curso pode proporcionar uma experiência de aprendizagem eficaz,útil e interessante. Concebido de forma interdisciplinar, o curso pode, assim, ser uma opção para o ensino de ML com foco na classificação de imagens com Deep Learning no Ensino Médio no Brasil em conformidade com diretrizes curriculares.
Palavras-chave:
Ensino, Ensino Médio, Machine Learning, Redes Neurais
Referências
Basili, V. R., Caldiera, G., Rombach, H. D. (1994) “Goal, Question Metric Paradigm”. In J. J. Marciniak, Encyclopedia of Software Engineering, 528-532. New York: Wiley-Interscience.
Bhatia, N. (2020) “Using Transfer Learning, Spectrogram Audio Classification, and MIT App Inventor to Facilitate Machine Learning Understanding”. Master thesis, Massachusetts Institute of Technology, Cambridge, MA, USA.
Branch, R. (2009). Instructional design: The ADDIE approach, Athens: Springer, 2nd edição.
Brooke, J. (1996). “SUS-A quick and dirty usability scale”. Usability Evaluation in Industry, 189(194), 4-7.
Burgsteiner, H., Kandlhofer, M., Steinbauer, G. (2016). “iRobot: Teaching the Basics of Artificial Intelligence in High Schools”, Proc. of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.
Cardozo, J. (2022) “Desenvolvimento de um Curso On-line para o Ensino de Machine Learning voltado à Classificação de Imagens no Ensino Médio”. TCC, UFSC, Brasil.
Davis, F. D. (1989). “Perceived usefulness, perceived ease of use, and user acceptance of information technology”. MIS quarterly, 319-340.
Estevez, J. G., Garate, G., Graña, M. (2019) “Gentle Introduction to Artificial Intelligence for High-School Students Using Scratch”. IEEE Access, 7.
Franz, G., Gresse von Wangenheim, C. Hauck, J. C. R. (2022) “VULCAN - A Visual Layer for Jupyter Notebook for Teaching Image Classification with Deep Learning to Novice”, submetido.
Gresse von Wangenheim, C. et al. (2022) “A Proposal for Performance-based Assessment of the Learning of Machine Learning Concepts and Practices in K-12”. Informatics in Education, 21 (3).
International Standard Organization (ISO). (2011). ISO/IEC 25010: Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – System and software quality models, Technical Report.
International Standard Organization (ISO). (1998). ISO/IEC 9241: Ergonomic requirements for office work with visual display terminals (VDTs) -- Part 11: Guidance on usability.
Long, D., Magerko, B. (2020) “What is AI Literacy? Competencies and Design Considerations”. Proc. of the Conference on Human Factors in Computing Systems. New York, NY, USA.
Marques, L., Gresse von Wangenheim, C., Hauck, J. (2020) “Ensino de Machine Learning na Educação Básica: um Mapeamento Sistemático do Estado da Arte”. Anais do Simpósio Brasileiro de Informática na Educação, Natal, Brasil.
MEC. (2017) “Base Nacional Comum Curricular", [link].
Mike, K., Hazan, T., Hazzan, O. (2020). “Equalizing Data Science Curriculum for Computer Science Pupils”. Proc. of the 20th Koli Calling International Conference on Computing Education Research, New York, NY, USA, 2020.
Mobasher, B., Dettori, L., Raicu, D. S., Settimi, R., Sonboli, N., Stettler, M. (2019). Data Science Summer Academy for Chicago Public School Students. ACM SIGKDD Explorations Newsletter, 21(1).
Neumann, M. (2019). AI education matters: a first introduction to modeling and learning using the data science workflow. AI Matters 5, 21–24.
Norouzi, N., Chaturvedi, S., Rutledge, M. (2020). Lessons Learned from Teaching Machine Learning and Natural Language Processing to High School Students. Proc. of the AAAI Conference on Artificial Intelligence, 34(09), 13397-13403.
Petersen, K. et al. (2008) “Systematic mapping studies in software engineering”. Proc. of the 12th International Conference on Evaluation and Assessment in Software Engineering, Bari, Italy, 68–77.
Salvador, G., et al. Avaliação de Aprendizagem de Machine Learning na Educação Básica: Um Mapeamento da Literatura. Anais do 29º WEI – Workshop sobre Educação em Computação, online, 2021.
Sociedade Brasileira de Computação (SBC). (2017) “Diretrizes para ensino de Computação na Educação Básica”, [link].
Touretzky, D. S., Gardner-McCune, C., Martin, F., Seehorn, D. (2019) “Envisioning AI for K-12: What Should Every Child Know about AI?”. Proc. of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA.
Bhatia, N. (2020) “Using Transfer Learning, Spectrogram Audio Classification, and MIT App Inventor to Facilitate Machine Learning Understanding”. Master thesis, Massachusetts Institute of Technology, Cambridge, MA, USA.
Branch, R. (2009). Instructional design: The ADDIE approach, Athens: Springer, 2nd edição.
Brooke, J. (1996). “SUS-A quick and dirty usability scale”. Usability Evaluation in Industry, 189(194), 4-7.
Burgsteiner, H., Kandlhofer, M., Steinbauer, G. (2016). “iRobot: Teaching the Basics of Artificial Intelligence in High Schools”, Proc. of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.
Cardozo, J. (2022) “Desenvolvimento de um Curso On-line para o Ensino de Machine Learning voltado à Classificação de Imagens no Ensino Médio”. TCC, UFSC, Brasil.
Davis, F. D. (1989). “Perceived usefulness, perceived ease of use, and user acceptance of information technology”. MIS quarterly, 319-340.
Estevez, J. G., Garate, G., Graña, M. (2019) “Gentle Introduction to Artificial Intelligence for High-School Students Using Scratch”. IEEE Access, 7.
Franz, G., Gresse von Wangenheim, C. Hauck, J. C. R. (2022) “VULCAN - A Visual Layer for Jupyter Notebook for Teaching Image Classification with Deep Learning to Novice”, submetido.
Gresse von Wangenheim, C. et al. (2022) “A Proposal for Performance-based Assessment of the Learning of Machine Learning Concepts and Practices in K-12”. Informatics in Education, 21 (3).
International Standard Organization (ISO). (2011). ISO/IEC 25010: Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – System and software quality models, Technical Report.
International Standard Organization (ISO). (1998). ISO/IEC 9241: Ergonomic requirements for office work with visual display terminals (VDTs) -- Part 11: Guidance on usability.
Long, D., Magerko, B. (2020) “What is AI Literacy? Competencies and Design Considerations”. Proc. of the Conference on Human Factors in Computing Systems. New York, NY, USA.
Marques, L., Gresse von Wangenheim, C., Hauck, J. (2020) “Ensino de Machine Learning na Educação Básica: um Mapeamento Sistemático do Estado da Arte”. Anais do Simpósio Brasileiro de Informática na Educação, Natal, Brasil.
MEC. (2017) “Base Nacional Comum Curricular", [link].
Mike, K., Hazan, T., Hazzan, O. (2020). “Equalizing Data Science Curriculum for Computer Science Pupils”. Proc. of the 20th Koli Calling International Conference on Computing Education Research, New York, NY, USA, 2020.
Mobasher, B., Dettori, L., Raicu, D. S., Settimi, R., Sonboli, N., Stettler, M. (2019). Data Science Summer Academy for Chicago Public School Students. ACM SIGKDD Explorations Newsletter, 21(1).
Neumann, M. (2019). AI education matters: a first introduction to modeling and learning using the data science workflow. AI Matters 5, 21–24.
Norouzi, N., Chaturvedi, S., Rutledge, M. (2020). Lessons Learned from Teaching Machine Learning and Natural Language Processing to High School Students. Proc. of the AAAI Conference on Artificial Intelligence, 34(09), 13397-13403.
Petersen, K. et al. (2008) “Systematic mapping studies in software engineering”. Proc. of the 12th International Conference on Evaluation and Assessment in Software Engineering, Bari, Italy, 68–77.
Salvador, G., et al. Avaliação de Aprendizagem de Machine Learning na Educação Básica: Um Mapeamento da Literatura. Anais do 29º WEI – Workshop sobre Educação em Computação, online, 2021.
Sociedade Brasileira de Computação (SBC). (2017) “Diretrizes para ensino de Computação na Educação Básica”, [link].
Touretzky, D. S., Gardner-McCune, C., Martin, F., Seehorn, D. (2019) “Envisioning AI for K-12: What Should Every Child Know about AI?”. Proc. of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA.
Publicado
31/07/2022
Como Citar
CARDOZO, Jacqueline; MARTINS, Ramon Mayor; WANGENHEIM, Christiane Gresse von.
ML4Teens - Introduzindo Machine Learning no Ensino Médio. In: WORKSHOP SOBRE EDUCAÇÃO EM COMPUTAÇÃO (WEI), 30. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 204-214.
ISSN 2595-6175.
DOI: https://doi.org/10.5753/wei.2022.222424.
