Simulando projetos de Machine Learning com dados e clientes reais em cursos de graduação: qual é a opinião dos estudantes sobre isto?
Resumo
Este trabalho avalia a Sprint Session do curso de Ciência da Computação do INSPER, iniciativa que aproxima estudantes da realidade de Machine Learning (ML) por meio de desafios baseados em demandas reais. A análise de três edições (2024-2 a 2025-2), envolvendo 74 alunos organizados em 18 equipes que atuaram em 3 projetos, indica, via análise qualitativa, que os estudantes valorizam a aplicação prática com dados reais, o contato corporativo e o desenvolvimento de competências em modelagem, MLOps e deploy. Embora persistam desafios como tempo limitado e complexidade dos dados, a iniciativa demonstra eficácia no desenvolvimento integrado de competências técnicas e profissionais essenciais a projetos de ML.Referências
Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., and Zimmermann, T. (2019). Software engineering for machine learning: A case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 291–300.
Barth, F., Burd, L., and Pimentel, M. (2012). Escritório de projetos: simulando o ambiente de projetos de software em cursos de tecnologia. In Anais do XX Workshop sobre Educação em Computação, pages 299–302, Porto Alegre, RS, Brasil. SBC.
Biswas, S., Wardat, M., and Rajan, H. (2022). The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large. In Proceedings of the 44th International Conference on Software Engineering, ICSE ’22, pages 2091–2103. ACM.
Dogan, G. (2023). Teaching machine learning with applied interdisciplinary real world projects. In Kinnaird, K. M., Steinbach, P., and Guhr, O., editors, Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, volume 207 of Proceedings of Machine Learning Research, pages 12–15. PMLR.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). The kdd process for extracting useful knowledge from volumes of data. Commun. ACM, 39(11):27–34.
Force, A. D. S. T. (2021). Computing competencies for undergraduate data science curricula. Association for Computing Machinery, New York, NY, USA.
Idowu, S., Sens, Y., Berger, T., Krueger, J., and Vierhauser, M. (2024). A Large-Scale Study of ML-Related Python Projects. In Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, SAC ’24, pages 1272–1281, New York, NY, USA. Association for Computing Machinery. event-place: Avila, Spain.
Idowu, S., Strüber, D., and Berger, T. (2022). Asset Management in Machine Learning: State-of-research and State-of-practice. ACM Comput. Surv., 55(7).
Lanubile, F., Martínez-Fernández, S., and Quaranta, L. (2023). Teaching mlops in higher education through project-based learning. In 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), page 95–100. IEEE.
Pozzorini, C., Andre, G., Coletta, T., Buisson, A., Bieler, J., Ferrer, L., Kempfer, R., Saintigny, P., Harlé, A., Vacirca, D., Barberis, M., Gilson, P., Roma, C., Saitta, A., Smith, E., Consales Barras, F., Ripol, L., Fritzsche, M., Marques, A. C., Alkodsi, A., Marin, R., Normanno, N., Grimm, C., Müllauer, L., Harter, P., Pignata, S., Gonzalez-Martin, A., Denison, U., Fujiwara, K., Vergote, I., Colombo, N., Willig, A., Pujade-Lauraine, E., Just, P.-A., Ray-Coquard, I., and Xu, Z. (2023). Giinger predicts homologous recombination deficiency and patient response to parpi treatment from shallow genomic profiles. Cell Reports Medicine, 4(12):101344.
Sasajima, M., Ishibashi, K., Yamamoto, T., Yumoto, T., Ohshima, H., Fujie, T., and Katoh, N. (2024). Is problem-based learning exercises using real data effective on the education of lower grades in the faculty of data science? findings on pbl exercises for five years. In 2024 International Conference on Machine Learning and Cybernetics (ICMLC), pages 398–404.
Schröer, C., Kruse, F., and Gómez, J. M. (2021). A systematic literature review on applying crisp-dm process model. Procedia Computer Science, 181:526–534. CENTERIS 2020 - International Conference on ENTERprise Information Systems / ProjMAN 2020 - International Conference on Project MANagement / HCist 2020 - International Conference on Health and Social Care Information Systems and Technologies 2020, CENTERIS/ProjMAN/HCist 2020.
Soares, L. P., Lima, L. C., and Silva, R. I. G. (2024). Navigating engineering capstone strategies.
Stodden, V. (2020). The data science life cycle. Communications of the ACM, 63(7):58–66.
Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, volume 1, pages 29–39. Manchester.
Barth, F., Burd, L., and Pimentel, M. (2012). Escritório de projetos: simulando o ambiente de projetos de software em cursos de tecnologia. In Anais do XX Workshop sobre Educação em Computação, pages 299–302, Porto Alegre, RS, Brasil. SBC.
Biswas, S., Wardat, M., and Rajan, H. (2022). The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large. In Proceedings of the 44th International Conference on Software Engineering, ICSE ’22, pages 2091–2103. ACM.
Dogan, G. (2023). Teaching machine learning with applied interdisciplinary real world projects. In Kinnaird, K. M., Steinbach, P., and Guhr, O., editors, Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, volume 207 of Proceedings of Machine Learning Research, pages 12–15. PMLR.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). The kdd process for extracting useful knowledge from volumes of data. Commun. ACM, 39(11):27–34.
Force, A. D. S. T. (2021). Computing competencies for undergraduate data science curricula. Association for Computing Machinery, New York, NY, USA.
Idowu, S., Sens, Y., Berger, T., Krueger, J., and Vierhauser, M. (2024). A Large-Scale Study of ML-Related Python Projects. In Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, SAC ’24, pages 1272–1281, New York, NY, USA. Association for Computing Machinery. event-place: Avila, Spain.
Idowu, S., Strüber, D., and Berger, T. (2022). Asset Management in Machine Learning: State-of-research and State-of-practice. ACM Comput. Surv., 55(7).
Lanubile, F., Martínez-Fernández, S., and Quaranta, L. (2023). Teaching mlops in higher education through project-based learning. In 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), page 95–100. IEEE.
Pozzorini, C., Andre, G., Coletta, T., Buisson, A., Bieler, J., Ferrer, L., Kempfer, R., Saintigny, P., Harlé, A., Vacirca, D., Barberis, M., Gilson, P., Roma, C., Saitta, A., Smith, E., Consales Barras, F., Ripol, L., Fritzsche, M., Marques, A. C., Alkodsi, A., Marin, R., Normanno, N., Grimm, C., Müllauer, L., Harter, P., Pignata, S., Gonzalez-Martin, A., Denison, U., Fujiwara, K., Vergote, I., Colombo, N., Willig, A., Pujade-Lauraine, E., Just, P.-A., Ray-Coquard, I., and Xu, Z. (2023). Giinger predicts homologous recombination deficiency and patient response to parpi treatment from shallow genomic profiles. Cell Reports Medicine, 4(12):101344.
Sasajima, M., Ishibashi, K., Yamamoto, T., Yumoto, T., Ohshima, H., Fujie, T., and Katoh, N. (2024). Is problem-based learning exercises using real data effective on the education of lower grades in the faculty of data science? findings on pbl exercises for five years. In 2024 International Conference on Machine Learning and Cybernetics (ICMLC), pages 398–404.
Schröer, C., Kruse, F., and Gómez, J. M. (2021). A systematic literature review on applying crisp-dm process model. Procedia Computer Science, 181:526–534. CENTERIS 2020 - International Conference on ENTERprise Information Systems / ProjMAN 2020 - International Conference on Project MANagement / HCist 2020 - International Conference on Health and Social Care Information Systems and Technologies 2020, CENTERIS/ProjMAN/HCist 2020.
Soares, L. P., Lima, L. C., and Silva, R. I. G. (2024). Navigating engineering capstone strategies.
Stodden, V. (2020). The data science life cycle. Communications of the ACM, 63(7):58–66.
Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, volume 1, pages 29–39. Manchester.
Publicado
19/07/2026
Como Citar
BARTH, Fabrício J.; MIRANDA, Fabio Roberto de; GRAGLIA, Marcelo.
Simulando projetos de Machine Learning com dados e clientes reais em cursos de graduação: qual é a opinião dos estudantes sobre isto?. In: WORKSHOP SOBRE EDUCAÇÃO EM COMPUTAÇÃO (WEI), 34. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 957-968.
ISSN 2595-6175.
DOI: https://doi.org/10.5753/wei.2026.22373.
