Avaliação de uma ferramenta de ensino de programação visando engajamento e utilidade
Resumo
Ferramentas computacionais inteligentes têm o potencial de transformar o ensino de programação, mas sua aceitação permanece desafiadora. Neste trabalho, abordamos questões relativas à adoção da plataforma educacional Machine Teaching (MT) para ensino de programação no Instituto de Computação da Universidade Federal do Rio de Janeiro. A pesquisa visou identificar os fatores que influenciam tanto a adoção quanto a resistência ao uso do MT por parte de alunos e professores, bem como a implementação de medidas para aumentar o engajamento da plataforma. Este artigo traz o relato de experiência de como os fatores que influenciam a adoção do MT foram acessados e a trajetória para elaboração de uma estratégia para aumentar a adesão e o engajamento dos usuários.Referências
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Brown, N. C. C., Altadmri, A., Sentance, S., e Kölling, M. (2018). Blackbox, five years on: An evaluation of a large-scale programming data collection project. In Proceedings of the 2018 ACM Conference on International Computing Education Research, ICER ’18, page 196–204, New York, NY, USA. Association for Computing Machinery.
Brown, N. C. C., Kölling, M., McCall, D., e Utting, I. (2014). Blackbox: a large scale repository of novice programmers’ activity. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE ’14, page 223–228, New York, NY, USA. Association for Computing Machinery.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3):319–340.
Delgado, C., da Silva, J., Mascarenhas, F., e Duboc, A. (2016). The teaching of functions as the first step to learn imperative programming. In Anais do XXIV Workshop sobre Educação em Computação, pages 2393–2402, Porto Alegre, RS, Brasil. Sociedade Brasileira de Computação - SBC.
Edwards, S. H. e Perez-Quinones, M. A. (2008). Web-CAT: automatically grading programming assignments. In Proceedings of the 13th Annual Conference on Innovation and Technology in Computer Science Education, page 328, Madrid, Spain.
Edwards, S. H., Tilden, D. S., e Allevato, A. (2014). Pythy: improving the introductory python programming experience. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE ’14, pages 641–646, Atlanta, GA, USA.
Fook, C. Y., Narasuman, S., Abdul Aziz, N., e Tau Han, C. (2021). Smartphone usage among university students. Asian Journal of University Education (AJUE), 7(1):282–291.
Galvão, L., Fernandes, D., e Gadelha, B. (2016). Juiz online como ferramenta de apoio a uma metodologia de ensino híbrido em programação. Anais do XXVII Simpósio Brasileiro de Informática na Educação (SBIE 2016), pages 140–149.
Hovemeyer, D. e Spacco, J. (2013). Cloudcoder: A web-based programming exercise system. Journal of Computing Sciences in Colleges, 28(3):30.
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., Rubio, M. A., Sheard, J., Skupas, B., Spacco, J., Szabo, C., e Toll, D. (2015). Educational data mining and learning analytics in programming: Literature review and case studies. In Proceedings of the 2015 ITiCSE on Working Group Reports, ITICSE-WGR ’15, page 41–63, New York, NY, USA. Association for Computing Machinery.
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Kumar, A. N. (2005). Generation of problems, answers, grade, and feedback—case study of a fully automated tutor. J. Educ. Resour. Comput., 5(3):3–es.
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Moraes, L. O., Delgado, C. A. D. M., Freire, J. P., e Pedreira, C. E. (2022). Machine teaching: uma ferramenta didática e de análise de dados para suporte a cursos introdutórios de programação. In Anais do II Simpósio Brasileiro de Educação em Computação, pages 213–223, Porto Alegre, RS, Brasil. SBC.
Moraes, L. O. e Pedreira, C. E. (2020). Designing an intelligent tutoring system across multiple classes. In 4th Educational Data Mining in Computer Science Education Workshop.
Moraes, L. O., Pedreira, C. E., Delgado, C., e Freire, J. P. (2021). Supporting decisions using educational data analysis. In Anais Estendidos do XXVII Simpósio Brasileiro de Sistemas Multimídia e Web, pages 99–102, Porto Alegre, RS, Brasil. SBC.
Panamalai Murali, K. (2016). CodeWorkout: Design and implementation of an online drill-and-practice system for introductory programming. Thesis, Virginia Tech.
Pärtel, M., Luukkainen, M., Vihavainen, A., e Vikberg, T. (2013). Test my code. Int. J. Technol. Enhanc. Learn., 5(3/4):271–283.
Persico, D., Manca, S., e Pozzi, F. (2014). Adapting the technology acceptance model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior, 30:614–622.
Rodrigues, H. S., da Silveira Santiago, E., Wanderley, G. M. d. C. X., Moraes, L. O., de Mello, C. E., Alvares, R. V., e dos Santos, R. P. (2024). Artificial intelligence algorithms to predict college students’ dropout: A systematic mapping study. ICAART (3), pages 344–351.
Scarpin, J. E., Mondini, V. E. D., e Scarpin, M. R. S. (2018). Technology acceptance factors and student retention in online courses. E-Journal of Business Education and Scholarship of Teaching, 12(3):44–68.
Sirotheau, S., Favero, E., Santos, J., e Balieiro, R. (2018). Labpy: Laboratório virtual de ensino em python. page 749.
Smith, K. (2012). Lessons learnt from literature on the diffusion of innovative learning and teaching practices in higher education. Innovations in Education and Teaching International - INNOV EDUC TEACH INT, 49:173–182.
Sorva, J. e Sirkiä, T. (2010). UUhistle: a software tool for visual program simulation. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, pages 49–54, Koli, Finland.
Tonin, N., Zanin, F., e Bez, J. L. (2012). Enhancing traditional Algorithms classes using URI Online Judge. In 2012 International Conference on e-Learning and e-Technologies in Education (ICEEE), pages 110–113.
Venkatesh, V., Morris, M. G., Davis, G. B., e Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3):425–478.
Wang, Y., Liu, C., e Tu, Y.-F. (2021). Factors affecting the adoption of ai-based applications in higher education: An analysis of teachers perspectives using structural equation modeling. Educational Technology Society, 24(3):116–129.
Xará, G., A. D. M. Delgado, C., M. de Farias, C., Folloni Guarilha, H., O. Moraes, L., Freire, J. P., e Nogueira Teixeira, E. (2025). Technical and tactical factors of an educational platform aiming to deal with a large number of students and inequalities when teaching programming for stem students. Revista Brasileira de Informática na Educação, 33:80–105.
Zingaro, D., Cherenkova, Y., Karpova, O., e Petersen, A. (2013). Facilitating code-writing in PI classes. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education, pages 585–590, Denver, CO, USA.
Bez, J. L., Ferreira, C. E., e Tonin, N. (2013). URI Online Judge Academic: A Tool for Professors. In Proceedings of the 2013 International Conference on Advanced ICT and Education, pages 744–747.
Brown, N. C. C., Altadmri, A., Sentance, S., e Kölling, M. (2018). Blackbox, five years on: An evaluation of a large-scale programming data collection project. In Proceedings of the 2018 ACM Conference on International Computing Education Research, ICER ’18, page 196–204, New York, NY, USA. Association for Computing Machinery.
Brown, N. C. C., Kölling, M., McCall, D., e Utting, I. (2014). Blackbox: a large scale repository of novice programmers’ activity. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE ’14, page 223–228, New York, NY, USA. Association for Computing Machinery.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3):319–340.
Delgado, C., da Silva, J., Mascarenhas, F., e Duboc, A. (2016). The teaching of functions as the first step to learn imperative programming. In Anais do XXIV Workshop sobre Educação em Computação, pages 2393–2402, Porto Alegre, RS, Brasil. Sociedade Brasileira de Computação - SBC.
Edwards, S. H. e Perez-Quinones, M. A. (2008). Web-CAT: automatically grading programming assignments. In Proceedings of the 13th Annual Conference on Innovation and Technology in Computer Science Education, page 328, Madrid, Spain.
Edwards, S. H., Tilden, D. S., e Allevato, A. (2014). Pythy: improving the introductory python programming experience. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE ’14, pages 641–646, Atlanta, GA, USA.
Fook, C. Y., Narasuman, S., Abdul Aziz, N., e Tau Han, C. (2021). Smartphone usage among university students. Asian Journal of University Education (AJUE), 7(1):282–291.
Galvão, L., Fernandes, D., e Gadelha, B. (2016). Juiz online como ferramenta de apoio a uma metodologia de ensino híbrido em programação. Anais do XXVII Simpósio Brasileiro de Informática na Educação (SBIE 2016), pages 140–149.
Hovemeyer, D. e Spacco, J. (2013). Cloudcoder: A web-based programming exercise system. Journal of Computing Sciences in Colleges, 28(3):30.
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., Rubio, M. A., Sheard, J., Skupas, B., Spacco, J., Szabo, C., e Toll, D. (2015). Educational data mining and learning analytics in programming: Literature review and case studies. In Proceedings of the 2015 ITiCSE on Working Group Reports, ITICSE-WGR ’15, page 41–63, New York, NY, USA. Association for Computing Machinery.
Instituto Brasileiro de Geografia e Estatística (2023). 161,6 milhões de pessoas com 10 anos ou mais de idade utilizaram a Internet no país, em 2022.
Karavirta, V., Helminen, J., e Ihantola, P. (2012). A mobile learning application for parsons problems with automatic feedback. In Proceedings of the 12th Koli Calling International Conference on Computing Education Research, Koli Calling ’12, page 11–18, New York, NY, USA. Association for Computing Machinery.
Kumar, A. N. (2005). Generation of problems, answers, grade, and feedback—case study of a fully automated tutor. J. Educ. Resour. Comput., 5(3):3–es.
Kurtz, R., de Macedo-Soares, T. D., Ferreira, J. B., de Freitas, A. S., e da Silva, J. F. (2015). Fatores de impacto na atitude e na intenção de uso do m-learning: um teste empírico. REAd. Rev. eletrôn. adm., 21(1).
Laurillard, D., Oliver, M., Wasson, B., e Hoppe, U. (2009). Implementing Technology-Enhanced Learning, pages 289–306. Springer Netherlands, Dordrecht.
Luxton-Reilly, A., Simon, Albluwi, I., Becker, B. A., Giannakos, M., Kumar, A. N., Ott, L., Paterson, J., Scott, M. J., Sheard, J., e Szabo, C. (2018). Introductory programming: A systematic literature review. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018 Companion, page 55–106, Larnaca, Cyprus.
Moraes, L. O., Delgado, C. A. D. M., Freire, J. P., e Pedreira, C. E. (2022). Machine teaching: uma ferramenta didática e de análise de dados para suporte a cursos introdutórios de programação. In Anais do II Simpósio Brasileiro de Educação em Computação, pages 213–223, Porto Alegre, RS, Brasil. SBC.
Moraes, L. O. e Pedreira, C. E. (2020). Designing an intelligent tutoring system across multiple classes. In 4th Educational Data Mining in Computer Science Education Workshop.
Moraes, L. O., Pedreira, C. E., Delgado, C., e Freire, J. P. (2021). Supporting decisions using educational data analysis. In Anais Estendidos do XXVII Simpósio Brasileiro de Sistemas Multimídia e Web, pages 99–102, Porto Alegre, RS, Brasil. SBC.
Panamalai Murali, K. (2016). CodeWorkout: Design and implementation of an online drill-and-practice system for introductory programming. Thesis, Virginia Tech.
Pärtel, M., Luukkainen, M., Vihavainen, A., e Vikberg, T. (2013). Test my code. Int. J. Technol. Enhanc. Learn., 5(3/4):271–283.
Persico, D., Manca, S., e Pozzi, F. (2014). Adapting the technology acceptance model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior, 30:614–622.
Rodrigues, H. S., da Silveira Santiago, E., Wanderley, G. M. d. C. X., Moraes, L. O., de Mello, C. E., Alvares, R. V., e dos Santos, R. P. (2024). Artificial intelligence algorithms to predict college students’ dropout: A systematic mapping study. ICAART (3), pages 344–351.
Scarpin, J. E., Mondini, V. E. D., e Scarpin, M. R. S. (2018). Technology acceptance factors and student retention in online courses. E-Journal of Business Education and Scholarship of Teaching, 12(3):44–68.
Sirotheau, S., Favero, E., Santos, J., e Balieiro, R. (2018). Labpy: Laboratório virtual de ensino em python. page 749.
Smith, K. (2012). Lessons learnt from literature on the diffusion of innovative learning and teaching practices in higher education. Innovations in Education and Teaching International - INNOV EDUC TEACH INT, 49:173–182.
Sorva, J. e Sirkiä, T. (2010). UUhistle: a software tool for visual program simulation. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, pages 49–54, Koli, Finland.
Tonin, N., Zanin, F., e Bez, J. L. (2012). Enhancing traditional Algorithms classes using URI Online Judge. In 2012 International Conference on e-Learning and e-Technologies in Education (ICEEE), pages 110–113.
Venkatesh, V., Morris, M. G., Davis, G. B., e Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3):425–478.
Wang, Y., Liu, C., e Tu, Y.-F. (2021). Factors affecting the adoption of ai-based applications in higher education: An analysis of teachers perspectives using structural equation modeling. Educational Technology Society, 24(3):116–129.
Xará, G., A. D. M. Delgado, C., M. de Farias, C., Folloni Guarilha, H., O. Moraes, L., Freire, J. P., e Nogueira Teixeira, E. (2025). Technical and tactical factors of an educational platform aiming to deal with a large number of students and inequalities when teaching programming for stem students. Revista Brasileira de Informática na Educação, 33:80–105.
Zingaro, D., Cherenkova, Y., Karpova, O., e Petersen, A. (2013). Facilitating code-writing in PI classes. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education, pages 585–590, Denver, CO, USA.
Publicado
20/07/2025
Como Citar
CRUZ, Amai Nikaido da; TEIXEIRA, Eldânae Nogueira; CARVALHO, Lucca; RODRIGUES, Henrique Soares; MORAES, Laura O.; DELGADO, Carla A. D. M..
Avaliação de uma ferramenta de ensino de programação visando engajamento e utilidade. In: WORKSHOP SOBRE EDUCAÇÃO EM COMPUTAÇÃO (WEI), 33. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1092-1104.
ISSN 2595-6175.
DOI: https://doi.org/10.5753/wei.2025.8944.
