Visual-AM: A Visual No-Code Web Tool to Support the Study of Machine Learning
Resumo
Esta pesquisa apresenta a Visual-AM, uma ferramenta web visual e no-code desenvolvida com base na Design Science Research Methodology para apoiar o estudo de aprendizado de máquina (AM). A ferramenta permite a exploração prática de modelos, independentemente do nível de experiência do usuário em programação. Sua avaliação foi realizada com graduandos voluntários do curso de Engenharia de Bioprocessos da Universidade Federal de Itajubá, por meio de questionários mistos que investigaram o perfil dos respondentes e suas experiências e percepções sobre o uso da ferramenta. Os resultados indicaram predominância de avaliações positivas, sugerindo boa aceitação e potencial como recurso de apoio didático ao estudo de AM, considerando os construtos “Facilidade de Uso”, “Fluidez Informacional”, “Engajamento”, “Sensação de Progresso” e “Contribuição dos Recursos Visuais”.
Palavras-chave:
Machine Learning, No-Code Tool, Educação em Computação
Referências
Abd Rahman, A. S. et al. (2021). "Rapid software framework for the implementation of machine learning classification models", International Journal of Emerging Technology and Advanced Engineering, v. 11, p. 8-18.
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Aeberhard, S. and Forina, M. (1991). "Wine", UCI Machine Learning Repository, Available in: [link]
Artusi, R. et al. (2002). "Bravais-Pearson and Spearman correlation coefficients: Meaning, test of hypothesis and confidence interval", The International Journal of Biological Markers, v. 17, n. 2, p. 148-151.
Askitas, N. (2024). "A hands-on machine learning primer for social scientists: Math, algorithms and code", IZA DP No. 17014. Bonn: Institute of Labor Economics (IZA).
Bäuerle, A. et al. (2022). "Symphony: Composing interactive interfaces for machine learning", In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1-14.
Brasil. (2016). "Resolução n° 510, de 07 de abril de 2016, Dispõe sobre as normas aplicáveis a pesquisas em Ciências Humanas e Sociais", Conselho Nacional de Saúde.
Burnett, M. M. (1999). "Visual Programming", In: WILEY Encyclopedia of Electrical and Electronics Engineering. [S. l.]: John Wiley & Sons, Inc., pages 1-13.
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, pages 1-8.
Carvalho, D. V. et al. (2019). "Machine learning interpretability: A survey on methods and metrics", Electronics, v. 8, n. 8, p. 832.
Charytanowicz, M. et al. (2012). "Seeds", UCI Machine Learning Repository, Available in: [link]
Clark, L. A. and Watson, D. (1995). "Constructing validity: Basic issues in objective scale development", Psychological Assessment, v. 7, n. 3, p. 309-319.
da Silva, D. W. et al. (2024). "Ferramentas visuais web para o estudo de aprendizado de máquina no ensino superior: Um mapeamento sistemático", In: Simpósio Brasileiro de Informática na Educação (SBIE), SBC, pages 264-275.
Deisenroth, M. P. et al. (2020). "Mathematics for machine learning", Cambridge University Press.
Estevez, J. et al. (2019). "Using Scratch to teach undergraduate students' skills on artificial intelligence", arXiv preprint arXiv:1904.00296.
Faulkner, L. (2003). "Beyond the five-user assumption: Benefits of increased sample sizes in usability testing", Behavior Research Methods, Instruments, & Computers, v. 35, n. 3, p. 379-383.
Ferdous, N. et al. (2023). "Mpropred: A machine learning (ML) driven web-app for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists", Plos One, v. 18, n. 6, p. e0287179.
Fisher, R. A. (1936). "The use of multiple measurements in taxonomic problems", Annals of Eugenics, v. 7, n. 2, p. 179-188.
Freitas Junior, V. et al. (2017). "Design Science Research Methodology enquanto estratégia metodológica para a pesquisa tecnológica", Revistas Espacios, v. 38, n. 6, p. 25.
Gama, C. L. G. (2007). "Método de construção de objetos de aprendizagem com aplicação em métodos numéricos", 182 f. Tese (Doutorado em Métodos Numéricos em Engenharia) – Setor de Tecnologia, Universidade Federal do Paraná, Curitiba.
Goh, H. et al. (2023). "Front-end deep learning web apps development and deployment: A review", Applied Intelligence, v. 53, n. 12, p. 15923-15945.
Gresse Von Wangenheim, C. 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.
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.
Jiang, Y. et al. (2024). "Graph4GUI: Graph neural networks for representing graphical user interfaces", In: Proceedings of the CHI Conference on Human Factors in Computing Systems, pages 1-18.
Kaggle. (2017). "Mall_Customers", Available in: [link]
Kahn, K. et al. (2020). "Deep learning programming by all", In: Proceedings of the Conference on Constructionism, Dublin, Ireland.
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.
Korus, K. et al. (2021). "Optimization of geometric parameters of arch bridges using visual programming FEM components and genetic algorithm", Engineering Structures, v. 241, p. 112465.
Lao, N. (2020). "Reorienting machine learning education towards tinkerers and ML-engaged citizens", Doctoral Thesis, Massachusetts Institute of Technology.
Lohweg, V. (2013). "Banknote Authentication", UCI Machine Learning Repository, Available in: [link]
Macefield, R. (2009). "How to specify the participant group size for usability studies: A practitioner's guide", Journal of Usability Studies, v. 4, n. 3, p. 22-45.
Malta, D. P. L. N. et al. (2025). "O papel da computação em nuvem na gestão e acessibilidade educacional", Missioneira, v. 27, n. 9, p. 409-423.
Mayer, R. E. (ed.). (2005). "The Cambridge handbook of multimedia learning", New York: Cambridge University Press.
Meyer Junior, L. and Billionniere, E. (2021). "AWS academy vs Microsoft Learn for educators vs IBM Skills Academy: The educators choice", In: Society for Information Technology & Teacher Education International Conference, Association for the Advancement of Computing in Education (AACE), pages 528-534.
Mohseni, S. et al. (2019). "Practical solutions for machine learning safety in autonomous vehicles", arXiv preprint arXiv:1912.09630.
Norman, G. (2010). "Likert scales, levels of measurement and the "laws" of statistics", Advances in Health Sciences Education, v. 15, n. 5, p. 625-632.
Paiva, A. C. R. et al. (2007). "Towards the integration of visual and formal models for GUI testing", Electronic Notes in Theoretical Computer Science, v. 190, n. 2, p. 99-111.
Pedregosa, F. et al. (2011). "Scikit-learn: Machine learning in Python", Journal of Machine Learning Research, v. 12, p. 2825–2830.
Peffers, K. et al. (2007). "A design science research methodology for information systems research", Journal of Management Information Systems, v. 24, n. 3, p. 45-77.
Pereira, D. E. F. and Seabra, R. D. (2023). "Open educational resource for studying algorithms and programming logic: An approach to the technical level integrated with secondary school", Informatics in Education, v. 22, p. 441-462.
Podrzaj, P. (2019). "A brief demonstration of some Python GUI libraries", In: Proceedings of the 8th International Conference on Informatics and Applications ICIA2019, pages 1-6.
Raff, E. et al. (2019). "PyLZJD: An easy to use tool for machine learning", UMBC Faculty Collection.
Sang, H. et al. (2023). "Analysis of the current situation of Big Data MOOCs in the intelligent era based on the perspective of improving the mental health of college students", Information, v. 14, n. 9, p. 511.
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).
Smilkov, D. et al. (2017). "Direct-manipulation visualization of deep networks", arXiv preprint arXiv:1708.03788.
Sullivan, G. M. and Artino Junior, A. R. (2013). "Analyzing and interpreting data from Likert-type scales", Journal of Graduate Medical Education, v. 5, n. 4, p. 541.
Sulmont, E. et al. (2019). "What is hard about teaching machine learning to non-majors? Insights from classifying instructors' learning goals", ACM Transactions on Computing Education (TOCE), v. 19, n. 4, p. 1-16.
Sultan, Nabil. (2013). "Cloud computing: A democratizing force?", International Journal of Information Management, v. 33, n. 5, p. 810-815.
Thin-Yin, L. and Jonathan, L. Y. (2020). "Simpler machine learning using spreadsheets: Neural network predict", European Journal of Formal Sciences and Engineering, v. 3, n. 2, p. 43-64.
UCI Machine Learning Repository. (2016). "Pima Indians Diabetes Database", Kaggle, 2016. Available in: [link]
Wang, A. and Durrant, J. D. (2002). "Open-source browser-based tools for structure-based computer-aided drug discovery", Molecules, v. 27, n. 14, p. 4623.
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.
Wu, J. et al. (2019). "Hyperparameter optimization for machine learning models based on Bayesian optimization", Journal of Electronic Science and Technology, v. 17, n. 1, p. 26-40.
Abell, T. et al. (2021). "Cloud computing as a key enabler for tech start-ups across Asia and the Pacific", Manila: Asian Development Bank, 38 p. (ADB Sustainable Development Working Paper Series, n. 79).
Aeberhard, S. and Forina, M. (1991). "Wine", UCI Machine Learning Repository, Available in: [link]
Artusi, R. et al. (2002). "Bravais-Pearson and Spearman correlation coefficients: Meaning, test of hypothesis and confidence interval", The International Journal of Biological Markers, v. 17, n. 2, p. 148-151.
Askitas, N. (2024). "A hands-on machine learning primer for social scientists: Math, algorithms and code", IZA DP No. 17014. Bonn: Institute of Labor Economics (IZA).
Bäuerle, A. et al. (2022). "Symphony: Composing interactive interfaces for machine learning", In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1-14.
Brasil. (2016). "Resolução n° 510, de 07 de abril de 2016, Dispõe sobre as normas aplicáveis a pesquisas em Ciências Humanas e Sociais", Conselho Nacional de Saúde.
Burnett, M. M. (1999). "Visual Programming", In: WILEY Encyclopedia of Electrical and Electronics Engineering. [S. l.]: John Wiley & Sons, Inc., pages 1-13.
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, pages 1-8.
Carvalho, D. V. et al. (2019). "Machine learning interpretability: A survey on methods and metrics", Electronics, v. 8, n. 8, p. 832.
Charytanowicz, M. et al. (2012). "Seeds", UCI Machine Learning Repository, Available in: [link]
Clark, L. A. and Watson, D. (1995). "Constructing validity: Basic issues in objective scale development", Psychological Assessment, v. 7, n. 3, p. 309-319.
da Silva, D. W. et al. (2024). "Ferramentas visuais web para o estudo de aprendizado de máquina no ensino superior: Um mapeamento sistemático", In: Simpósio Brasileiro de Informática na Educação (SBIE), SBC, pages 264-275.
Deisenroth, M. P. et al. (2020). "Mathematics for machine learning", Cambridge University Press.
Estevez, J. et al. (2019). "Using Scratch to teach undergraduate students' skills on artificial intelligence", arXiv preprint arXiv:1904.00296.
Faulkner, L. (2003). "Beyond the five-user assumption: Benefits of increased sample sizes in usability testing", Behavior Research Methods, Instruments, & Computers, v. 35, n. 3, p. 379-383.
Ferdous, N. et al. (2023). "Mpropred: A machine learning (ML) driven web-app for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists", Plos One, v. 18, n. 6, p. e0287179.
Fisher, R. A. (1936). "The use of multiple measurements in taxonomic problems", Annals of Eugenics, v. 7, n. 2, p. 179-188.
Freitas Junior, V. et al. (2017). "Design Science Research Methodology enquanto estratégia metodológica para a pesquisa tecnológica", Revistas Espacios, v. 38, n. 6, p. 25.
Gama, C. L. G. (2007). "Método de construção de objetos de aprendizagem com aplicação em métodos numéricos", 182 f. Tese (Doutorado em Métodos Numéricos em Engenharia) – Setor de Tecnologia, Universidade Federal do Paraná, Curitiba.
Goh, H. et al. (2023). "Front-end deep learning web apps development and deployment: A review", Applied Intelligence, v. 53, n. 12, p. 15923-15945.
Gresse Von Wangenheim, C. 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.
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.
Jiang, Y. et al. (2024). "Graph4GUI: Graph neural networks for representing graphical user interfaces", In: Proceedings of the CHI Conference on Human Factors in Computing Systems, pages 1-18.
Kaggle. (2017). "Mall_Customers", Available in: [link]
Kahn, K. et al. (2020). "Deep learning programming by all", In: Proceedings of the Conference on Constructionism, Dublin, Ireland.
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.
Korus, K. et al. (2021). "Optimization of geometric parameters of arch bridges using visual programming FEM components and genetic algorithm", Engineering Structures, v. 241, p. 112465.
Lao, N. (2020). "Reorienting machine learning education towards tinkerers and ML-engaged citizens", Doctoral Thesis, Massachusetts Institute of Technology.
Lohweg, V. (2013). "Banknote Authentication", UCI Machine Learning Repository, Available in: [link]
Macefield, R. (2009). "How to specify the participant group size for usability studies: A practitioner's guide", Journal of Usability Studies, v. 4, n. 3, p. 22-45.
Malta, D. P. L. N. et al. (2025). "O papel da computação em nuvem na gestão e acessibilidade educacional", Missioneira, v. 27, n. 9, p. 409-423.
Mayer, R. E. (ed.). (2005). "The Cambridge handbook of multimedia learning", New York: Cambridge University Press.
Meyer Junior, L. and Billionniere, E. (2021). "AWS academy vs Microsoft Learn for educators vs IBM Skills Academy: The educators choice", In: Society for Information Technology & Teacher Education International Conference, Association for the Advancement of Computing in Education (AACE), pages 528-534.
Mohseni, S. et al. (2019). "Practical solutions for machine learning safety in autonomous vehicles", arXiv preprint arXiv:1912.09630.
Norman, G. (2010). "Likert scales, levels of measurement and the "laws" of statistics", Advances in Health Sciences Education, v. 15, n. 5, p. 625-632.
Paiva, A. C. R. et al. (2007). "Towards the integration of visual and formal models for GUI testing", Electronic Notes in Theoretical Computer Science, v. 190, n. 2, p. 99-111.
Pedregosa, F. et al. (2011). "Scikit-learn: Machine learning in Python", Journal of Machine Learning Research, v. 12, p. 2825–2830.
Peffers, K. et al. (2007). "A design science research methodology for information systems research", Journal of Management Information Systems, v. 24, n. 3, p. 45-77.
Pereira, D. E. F. and Seabra, R. D. (2023). "Open educational resource for studying algorithms and programming logic: An approach to the technical level integrated with secondary school", Informatics in Education, v. 22, p. 441-462.
Podrzaj, P. (2019). "A brief demonstration of some Python GUI libraries", In: Proceedings of the 8th International Conference on Informatics and Applications ICIA2019, pages 1-6.
Raff, E. et al. (2019). "PyLZJD: An easy to use tool for machine learning", UMBC Faculty Collection.
Sang, H. et al. (2023). "Analysis of the current situation of Big Data MOOCs in the intelligent era based on the perspective of improving the mental health of college students", Information, v. 14, n. 9, p. 511.
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).
Smilkov, D. et al. (2017). "Direct-manipulation visualization of deep networks", arXiv preprint arXiv:1708.03788.
Sullivan, G. M. and Artino Junior, A. R. (2013). "Analyzing and interpreting data from Likert-type scales", Journal of Graduate Medical Education, v. 5, n. 4, p. 541.
Sulmont, E. et al. (2019). "What is hard about teaching machine learning to non-majors? Insights from classifying instructors' learning goals", ACM Transactions on Computing Education (TOCE), v. 19, n. 4, p. 1-16.
Sultan, Nabil. (2013). "Cloud computing: A democratizing force?", International Journal of Information Management, v. 33, n. 5, p. 810-815.
Thin-Yin, L. and Jonathan, L. Y. (2020). "Simpler machine learning using spreadsheets: Neural network predict", European Journal of Formal Sciences and Engineering, v. 3, n. 2, p. 43-64.
UCI Machine Learning Repository. (2016). "Pima Indians Diabetes Database", Kaggle, 2016. Available in: [link]
Wang, A. and Durrant, J. D. (2002). "Open-source browser-based tools for structure-based computer-aided drug discovery", Molecules, v. 27, n. 14, p. 4623.
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.
Wu, J. et al. (2019). "Hyperparameter optimization for machine learning models based on Bayesian optimization", Journal of Electronic Science and Technology, v. 17, n. 1, p. 26-40.
Publicado
05/10/2026
Como Citar
DA SILVA, Denis Willian; BARBOSA, Luiz Carlos Bertucci; SEABRA, Rodrigo Duarte.
Visual-AM: A Visual No-Code Web Tool to Support the Study of Machine Learning. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 37. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 100-115.
DOI: https://doi.org/10.5753/sbie.2026.26716.
