Exploring Non-CS Learners’ Experience in Brazil
Resumo
Non-CS learners are increasingly interested in programming for career opportunities, productivity, and better communication with technical teams. Despite well-known challenges like logical thinking, unique issues for non-CS learners remain underexplored. Purpose: This study examines the motivations, experiences, and challenges of Brazilian non-CS learners, aiming to inform educational strategies. Methodology: We conducted an open-ended survey with a diverse sample of 36 considered respondents, analyzing data through open coding. Findings: Key motivations include career advancement and job performance, with a preference for self-learning. However, non-CS learners encounter challenges with knowledge organization, discipline, and technical comprehension. his highlights the need for adaptable and personalized educational solutions that effectively support learners from diverse backgrounds. Recommendations: Students can leverage their original field’s expertise when transitioning careers or selecting relevant technologies enhancing future roles. Companies should support diverse backgrounds and provide tech training to non-technical staff to improve communication and productivity. Academic institutions may provide optional, field-integrated CS courses to prepare students with practical, applicable skills.Referências
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Guo, P. J. (2017). Older adults learning computer programming: Motivations, frustrations, and design opportunities. In Proceedings of the 2017 chi conference on human factors in computing systems, pages 7070–7083.
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Spangsberg, T. H. and Brynskov, M. (2017). Code-labelling: A teaching activity encouraging deep learning in a non-stem introductory programming course. In 2017 12th International Conference on Computer Science and Education (ICCSE), pages 95–100. IEEE.
Stout, J. G. and Wright, H. M. (2016). Lesbian, gay, bisexual, transgender, and queer students’ sense of belonging in computing: An intersectional approach. Computing in Science & Engineering, 18(3):24–30.
Tsai, M.-H., Huang, C.-H., and Zeng, J.-Y. (2006). Game programming courses for non programmers. In Proceedings of the 2006 international Conference on Game Research and Development, pages 219–223.
Tucker, A. (2003). A model curriculum for k–12 computer science: Final report of the acm k–12 task force curriculum committee. Technical report, New York, NY, USA.
Vicari, R. M., Moreira, A. F., and Menezes, P. F. B. (2018). Pensamento computacional: revisão bibliográfica.
Wang, A. Y., Mitts, R., Guo, P. J., and Chilana, P. K. (2018). Mismatch of expectations: How modern learning resources fail conversational programmers. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pages 1–13.
Wing, J. M. (2006). Computational thinking. Commun. ACM, 49(3):33–35.
Xu, Z., Shen, X., Lin, S., and Zhang, F. (2021). Using visualization to teach an introductory programming course with python. In 2021 11th International Conference on Information Technology in Medicine and Education (ITME), pages 514–518. IEEE.
Bart, A. C., Tibau, J., Kafura, D., Shaffer, C. A., and Tilevich, E. (2017). Design and evaluation of a block-based environment with a data science context. IEEE Transactions on Emerging Topics in Computing, 8(1):182–192.
Bennedsen, J. and Caspersen, M. E. (2007). Failure rates in introductory programming. AcM SIGcSE Bulletin, 39(2):32–36.
Campos, J. M., Lozano, E. A., Urzúa, J., and Calderón, J. G. (2021). Challenge based learning: A fast track to introduce engineering students to data science. In 2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop, pages 1–6. IEEE.
Carr, V., Jones, M., and Wei, B. (2020a). Interdisciplinary computing: Applied computing for behavioral and social sciences. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education, pages 400–406.
Carr, V., Jones, M., and Wei, B. (2020b). Interdisciplinary computing: Applied computing for behavioral and social sciences. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education, pages 400–406.
Chilana, P. K., Alcock, C., Dembla, S., Ho, A., Hurst, A., Armstrong, B., and Guo, P. J. (2015). Perceptions of non-cs majors in intro programming: The rise of the conversational programmer. In 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pages 251–259. IEEE.
Chilana, P. K., Singh, R., and Guo, P. J. (2016). Understanding conversational programmers: A perspective from the software industry. In Proceedings of the 2016 CHI conference on human factors in computing systems, pages 1462–1472.
Christensen, I. M., Marcher, M. H., Grabarczyk, P., Graversen, T., and Brabrand, C. (2021). Computing educational activities involving people rather than things appeal more to women (recruitment perspective). In Proceedings of the 17th ACM Conference on International Computing Education Research, ICER 2021, page 127–144, New York, NY, USA. Association for Computing Machinery.
Cunningham, K., Ericson, B. J., Agrawal Bejarano, R., and Guzdial, M. (2021). Avoiding the turing tarpit: Learning conversational programming by starting from code’s purpose. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–15.
da Silva, T. R., Barros, I. S., Sousa, L. K. D. S., Sá, A. L. D., Silva, A. F. M., Araujo, M. C. S., and da Silva Aranha, E. H. (2021). Um mapeamento sistemático sobre o ensino e aprendizagem de programação. Revista Novas Tecnologias na Educação, 19(1):156–165.
Guo, P. J. (2017). Older adults learning computer programming: Motivations, frustrations, and design opportunities. In Proceedings of the 2017 chi conference on human factors in computing systems, pages 7070–7083.
Kakeshita, T. (2017). National survey of japanese universities on computing education: Analysis of non-it departments and courses. In 2017 Twelfth International Conference on Digital Information Management (ICDIM), pages 86–91. IEEE.
Madden, T. (2022). Why now is a good time for a career change to tech. [link] [Accessed: 2024-04-12].
Menkhoff, T. and Lydia Teo, Y. Q. (2022). Engaging undergraduate students in an introductory ai course through a knowledge-based chatbot workshop. In Proceedings of the 6th International Conference on Information System and Data Mining, pages 119–125.
Merriam, S. B. and Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation. John Wiley & Sons.
Mironova, O., Amitan, I., Vendelin, J., Vilipõld, J., and Saar, M. (2016). Teaching programming basics for first year non-it students. In 2016 IEEE Global Engineering Education Conference (EDUCON), pages 15–19. IEEE.
Pena, J., Hanrahan, B. V., Rosson, M. B., and Cole, C. (2021). After-hours learning: Workshops for professional women to learn web development. ACM Transactions on Computing Education (TOCE), 21(2):1–31.
Rountree, N., Rountree, J., Robins, A., and Hannah, R. (2004). Interacting factors that predict success and failure in a cs1 course. ACM SIGCSE Bulletin, 36(4):101–104.
Singh, V. K., Chayko, M., Inamdar, R., and Floegel, D. (2020). Female librarians and male computer programmers? gender bias in occupational images on digital media platforms. Journal of the Association for Information Science and Technology, 71(11):1281–1294.
Spangsberg, T. H. and Brynskov, M. (2017). Code-labelling: A teaching activity encouraging deep learning in a non-stem introductory programming course. In 2017 12th International Conference on Computer Science and Education (ICCSE), pages 95–100. IEEE.
Stout, J. G. and Wright, H. M. (2016). Lesbian, gay, bisexual, transgender, and queer students’ sense of belonging in computing: An intersectional approach. Computing in Science & Engineering, 18(3):24–30.
Tsai, M.-H., Huang, C.-H., and Zeng, J.-Y. (2006). Game programming courses for non programmers. In Proceedings of the 2006 international Conference on Game Research and Development, pages 219–223.
Tucker, A. (2003). A model curriculum for k–12 computer science: Final report of the acm k–12 task force curriculum committee. Technical report, New York, NY, USA.
Vicari, R. M., Moreira, A. F., and Menezes, P. F. B. (2018). Pensamento computacional: revisão bibliográfica.
Wang, A. Y., Mitts, R., Guo, P. J., and Chilana, P. K. (2018). Mismatch of expectations: How modern learning resources fail conversational programmers. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pages 1–13.
Wing, J. M. (2006). Computational thinking. Commun. ACM, 49(3):33–35.
Xu, Z., Shen, X., Lin, S., and Zhang, F. (2021). Using visualization to teach an introductory programming course with python. In 2021 11th International Conference on Information Technology in Medicine and Education (ITME), pages 514–518. IEEE.
Publicado
07/04/2025
Como Citar
GOMES, Renata Faria; SANTOS JUNIOR, Ricardo Ferreira Dos; GARCIA, Vinícius Cardoso.
Exploring Non-CS Learners’ Experience in Brazil. In: SIMPÓSIO BRASILEIRO DE EDUCAÇÃO EM COMPUTAÇÃO (EDUCOMP), 5. , 2025, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 15-26.
DOI: https://doi.org/10.5753/educomp.2025.4889.