Ferramentas Visuais Web para o Estudo de Aprendizado de Máquina no Ensino Superior: Um Mapeamento Sistemático
Resumo
O mapeamento sistemático conduzido neste estudo identificou oito ferramentas visuais web para o estudo de aprendizado de máquina (AM) no ensino superior, com destaque para suas características educacionais, recursos de AM oferecidos, elementos visuais utilizados para a facilitação dos estudos e metodologias de avaliação. Os resultados revelaram que as ferramentas não requerem experiência em AM pelos usuários e que a maioria delas serve para a demonstração do funcionamento de redes neurais, sendo a classificação de imagens a tarefa mais comumente abordada. A principal contribuição do artigo reside na caracterização de ferramentas visuais web acessíveis para o estudo de AM, visando auxiliar educadores e estudantes a selecionarem aquelas que mais se adaptem aos seus interesses e contextos de estudo.
Palavras-chave:
ferramentas visuais web, aprendizado de máquina, mapeamento sistemático
Referências
Carney, M. et al. (2020). “Teachable Machine: Approachable web-based tool for exploring machine learning classification”, In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, p. 1-8.
Estevez, J. et al. (2019). Using Scratch to teach undergraduate students’ skills on artificial intelligence, arXiv preprint arXiv:1904.00296.
Goodfellow, I. et al. (2016). Deep learning, The Mit Press.
Harley, A. W. (2015). “An interactive node-link visualization of convolutional neural networks”, In: Bebis, G. et al. Advances in Visual Computing, ISVC 2015, Lecture Notes in Computer Science, vol 9474. Springer, Cham.
Kahng, M. et al. (2018). “GAN lab: Understanding complex deep generative models using interactive visual experimentation”, IEEE Transactions on Visualization and Computer Graphics, v. 25, n. 1, p. 310-320.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering, EBSE Technical Report.
Kahn, K. et al. (2020). Deep learning programming by all, Constructionism.
Lalitha, T. B. and Sreeja, P. S. (2021). “Recommendation system based on machine learning and deep learning in varied perspectives: A systematic review”, In: Kaiser, M. S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020), Lecture Notes in Networks and Systems, v. 190, Springer, Singapore.
Mitchell, T. M. (1997). Machine learning, McGraw-Hill.
Parekh, D. et al. (2022). “Review on autonomous vehicles: Progress, methods and challenges. Electronics”, v. 11, p. 2162.
Petersen, K. et al. (2015). “Guidelines for conducting systematic mapping studies in software engineering: an update”, Information and Software Technology, v. 64, p. 1-18.
Raschka, S. et al. (2020). “Machine learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence”, Information, v. 11, n. 4, p. 193.
Schultze, S. et al. (2020). “Demystifying deep learning: Developing a learning App for beginners to gain practical experience”, In: Proceedings of the Mensch und Computer 2020 Workshop on User-Centered Artificial Intelligence (UCAI 2020).
Silva Filho, F. R. et al. (2023). “Uso de aprendizado de máquina em fóruns de ambientes virtuais de aprendizagem: Uma revisão sistemática de literatura”, RENOTE, v. 21, n. 2, p. 220–233.
Smilkov, D. et al. (2017). Direct-manipulation visualization of deep networks, arXiv preprint arXiv:1708.03788.
Wang, Z. J. et al. (2020). “CNN explainer: Learning convolutional neural networks with interactive visualization”, IEEE Transactions on Visualization and Computer Graphics, v. 27, n. 2, p. 1396-1406.
Wangenheim, C. G. V. et al. (2021). “Visual tools for teaching machine learning in K‑12: A ten‑year systematic mapping”, Education and Information Technologies, v. 26, n. 5, p. 5733–5778, 2021.
Zhang, A. et al. (2022). “Shifting machine learning for healthcare from development to deployment and from models to data”, Nature Biomedical Engineering, v. 6, p. 1330–1345.
Estevez, J. et al. (2019). Using Scratch to teach undergraduate students’ skills on artificial intelligence, arXiv preprint arXiv:1904.00296.
Goodfellow, I. et al. (2016). Deep learning, The Mit Press.
Harley, A. W. (2015). “An interactive node-link visualization of convolutional neural networks”, In: Bebis, G. et al. Advances in Visual Computing, ISVC 2015, Lecture Notes in Computer Science, vol 9474. Springer, Cham.
Kahng, M. et al. (2018). “GAN lab: Understanding complex deep generative models using interactive visual experimentation”, IEEE Transactions on Visualization and Computer Graphics, v. 25, n. 1, p. 310-320.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering, EBSE Technical Report.
Kahn, K. et al. (2020). Deep learning programming by all, Constructionism.
Lalitha, T. B. and Sreeja, P. S. (2021). “Recommendation system based on machine learning and deep learning in varied perspectives: A systematic review”, In: Kaiser, M. S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020), Lecture Notes in Networks and Systems, v. 190, Springer, Singapore.
Mitchell, T. M. (1997). Machine learning, McGraw-Hill.
Parekh, D. et al. (2022). “Review on autonomous vehicles: Progress, methods and challenges. Electronics”, v. 11, p. 2162.
Petersen, K. et al. (2015). “Guidelines for conducting systematic mapping studies in software engineering: an update”, Information and Software Technology, v. 64, p. 1-18.
Raschka, S. et al. (2020). “Machine learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence”, Information, v. 11, n. 4, p. 193.
Schultze, S. et al. (2020). “Demystifying deep learning: Developing a learning App for beginners to gain practical experience”, In: Proceedings of the Mensch und Computer 2020 Workshop on User-Centered Artificial Intelligence (UCAI 2020).
Silva Filho, F. R. et al. (2023). “Uso de aprendizado de máquina em fóruns de ambientes virtuais de aprendizagem: Uma revisão sistemática de literatura”, RENOTE, v. 21, n. 2, p. 220–233.
Smilkov, D. et al. (2017). Direct-manipulation visualization of deep networks, arXiv preprint arXiv:1708.03788.
Wang, Z. J. et al. (2020). “CNN explainer: Learning convolutional neural networks with interactive visualization”, IEEE Transactions on Visualization and Computer Graphics, v. 27, n. 2, p. 1396-1406.
Wangenheim, C. G. V. et al. (2021). “Visual tools for teaching machine learning in K‑12: A ten‑year systematic mapping”, Education and Information Technologies, v. 26, n. 5, p. 5733–5778, 2021.
Zhang, A. et al. (2022). “Shifting machine learning for healthcare from development to deployment and from models to data”, Nature Biomedical Engineering, v. 6, p. 1330–1345.
Publicado
04/11/2024
Como Citar
SILVA, Denis W. da; BARBOSA, Luiz Carlos B.; SEABRA, Rodrigo D..
Ferramentas Visuais Web para o Estudo de Aprendizado de Máquina no Ensino Superior: Um Mapeamento Sistemático. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 35. , 2024, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 264-275.
DOI: https://doi.org/10.5753/sbie.2024.242426.
