Investigação da Relação entre Traços de Personalidade e Distrações no Curso de Introdução à Programação
Resumo
Aprender CS1 é desafiador, pois exige foco e prática. Estudos mostram que distrações em sala de aula e traços de personalidade afetam o desempenho, mas a pesquisa sobre seu impacto em programação introdutória é limitada. Este artigo utilizou um questionário e análises estatísticas para identificar distrações comuns e sua correlação com os traços de personalidade do modelo Big Five. Os resultados revelaram que a fadiga e a temperatura ambiente são as principais distrações, com correlações moderadas entre os traços de personalidade e distrações específicas.Referências
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Anderson, E., Vasiliou, C., and Crick, T. (2022). Co-designing classroom practice to improve student attention and engagement in computer science degree programmes. In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 2, pages 629–629.
Brandt, N. D., Lechner, C. M., Tetzner, J., and Rammstedt, B. (2020). Personality, cognitive ability, and academic performance: Differential associations across school subjects and school tracks. Journal of personality, 88(2):249–265.
Brandt, N. D., Spengler, M., Lechner, C. M., Göllner, R., and Wagner, J. (2021). Associations between a multifaceted personality and academic performance in secondary school. European Journal of Personality, page 08902070241272202.
Cheah, C. S. (2020). Factors contributing to the difficulties in teaching and learning of computer programming: A literature review. Contemporary Educational Technology, 12(2).
Dinica, K., Demenescu, L. R., Lord, A., Krause, A. L., Kaiser, R., Horn, D., Metzger, C. D., and Walter, M. (2016). Self-directedness and the susceptibility to distraction by saliency. Cognition and Emotion, 30(8):1461–1469.
Donnellan, M. B., Oswald, F. L., Baird, B. M., and Lucas, R. E. (2006). The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors of personality. Psychological Assessment, 18:192–203.
Gibbons, J. D. and Chakraborti, S. (2014). Nonparametric statistical inference: revised and expanded. CRC press.
Liao, C.-H. and Wu, J.-Y. (2022). Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance. Computers & Education, 190:104599.
Medeiros, R. P., Ramalho, G., and Falcão, T. P. (2019). 3. a systematic literature review on teaching and learning introductory programming in higher education. IEEE Transactions on Education.
Medeiros, R. P., Ramalho, G. L., and Falcão, T. P. (2018). A systematic literature review on teaching and learning introductory programming in higher education. IEEE Transactions on Education, 62(2):77–90.
Oliveira, J. P. (2019). Psychometric properties of the portuguese version of the mini-ipip five-factor model personality scale. Current Psychology, 38:432–439.
Omer, U., Farooq, M. S., and Abid, A. (2021). Introductory programming course: review and future implications. PeerJ Computer Science, 7:e647.
Pérez-Juárez, M. Á., González-Ortega, D., and Aguiar-Pérez, J. M. (2023). Digital distractions from the point of view of higher education students. Sustainability, 15(7):6044.
Piwek, P. and Savage, S. (2020). Challenges with learning to program and problem solve: An analysis of student online discussions. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education, pages 494–499.
Seddigh, A., Berntson, E., Platts, L. G., and Westerlund, H. (2016). Does personality have a different impact on self-rated distraction, job satisfaction, and job performance in different office types? PloS one, 11(5):e0155295.
Shapiro, S. S. and Wilk, M. (1972). An analysis of variance test for the exponential distribution (complete samples). Technometrics, 14(2):355–370.
Wang, C.-H., Salisbury-Glennon, J. D., Dai, Y., Lee, S., and Dong, J. (2022). Empowering college students to decrease digital distraction through the use of self-regulated learning strategies. Contemporary Educational Technology, 14(4).
Wang, J., Tigelaar, D. E., Zhou, T., and Admiraal, W. (2023). The effects of mobile technology usage on cognitive, affective, and behavioural learning outcomes in primary and secondary education: A systematic review with meta-analysis. Journal of Computer Assisted Learning, 39(2):301–328.
Widiger, T. A. and Crego, C. (2019). The five factor model of personality structure: an update. World Psychiatry, 18(3):271.
Wiwatowska, E., Pietruch, M., Katafoni, P., and Michałowski, J. M. (2023). “i can’t focus now, i will study tomorrow”-the link between academic procrastination and resistance to distraction. Learning and Individual Differences, 107:102364.
Yang, R., Wibowo, S., Mubarak, S., and Rahamathulla, M. (2024). How do university students’ personalities affect their engagement, satisfaction, and stickiness toward e-learning?: evidence from australia. Interactive Learning Environments, pages 1–23.
Anderson, E., Vasiliou, C., and Crick, T. (2022). Co-designing classroom practice to improve student attention and engagement in computer science degree programmes. In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 2, pages 629–629.
Brandt, N. D., Lechner, C. M., Tetzner, J., and Rammstedt, B. (2020). Personality, cognitive ability, and academic performance: Differential associations across school subjects and school tracks. Journal of personality, 88(2):249–265.
Brandt, N. D., Spengler, M., Lechner, C. M., Göllner, R., and Wagner, J. (2021). Associations between a multifaceted personality and academic performance in secondary school. European Journal of Personality, page 08902070241272202.
Cheah, C. S. (2020). Factors contributing to the difficulties in teaching and learning of computer programming: A literature review. Contemporary Educational Technology, 12(2).
Dinica, K., Demenescu, L. R., Lord, A., Krause, A. L., Kaiser, R., Horn, D., Metzger, C. D., and Walter, M. (2016). Self-directedness and the susceptibility to distraction by saliency. Cognition and Emotion, 30(8):1461–1469.
Donnellan, M. B., Oswald, F. L., Baird, B. M., and Lucas, R. E. (2006). The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors of personality. Psychological Assessment, 18:192–203.
Gibbons, J. D. and Chakraborti, S. (2014). Nonparametric statistical inference: revised and expanded. CRC press.
Liao, C.-H. and Wu, J.-Y. (2022). Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance. Computers & Education, 190:104599.
Medeiros, R. P., Ramalho, G., and Falcão, T. P. (2019). 3. a systematic literature review on teaching and learning introductory programming in higher education. IEEE Transactions on Education.
Medeiros, R. P., Ramalho, G. L., and Falcão, T. P. (2018). A systematic literature review on teaching and learning introductory programming in higher education. IEEE Transactions on Education, 62(2):77–90.
Oliveira, J. P. (2019). Psychometric properties of the portuguese version of the mini-ipip five-factor model personality scale. Current Psychology, 38:432–439.
Omer, U., Farooq, M. S., and Abid, A. (2021). Introductory programming course: review and future implications. PeerJ Computer Science, 7:e647.
Pérez-Juárez, M. Á., González-Ortega, D., and Aguiar-Pérez, J. M. (2023). Digital distractions from the point of view of higher education students. Sustainability, 15(7):6044.
Piwek, P. and Savage, S. (2020). Challenges with learning to program and problem solve: An analysis of student online discussions. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education, pages 494–499.
Seddigh, A., Berntson, E., Platts, L. G., and Westerlund, H. (2016). Does personality have a different impact on self-rated distraction, job satisfaction, and job performance in different office types? PloS one, 11(5):e0155295.
Shapiro, S. S. and Wilk, M. (1972). An analysis of variance test for the exponential distribution (complete samples). Technometrics, 14(2):355–370.
Wang, C.-H., Salisbury-Glennon, J. D., Dai, Y., Lee, S., and Dong, J. (2022). Empowering college students to decrease digital distraction through the use of self-regulated learning strategies. Contemporary Educational Technology, 14(4).
Wang, J., Tigelaar, D. E., Zhou, T., and Admiraal, W. (2023). The effects of mobile technology usage on cognitive, affective, and behavioural learning outcomes in primary and secondary education: A systematic review with meta-analysis. Journal of Computer Assisted Learning, 39(2):301–328.
Widiger, T. A. and Crego, C. (2019). The five factor model of personality structure: an update. World Psychiatry, 18(3):271.
Wiwatowska, E., Pietruch, M., Katafoni, P., and Michałowski, J. M. (2023). “i can’t focus now, i will study tomorrow”-the link between academic procrastination and resistance to distraction. Learning and Individual Differences, 107:102364.
Yang, R., Wibowo, S., Mubarak, S., and Rahamathulla, M. (2024). How do university students’ personalities affect their engagement, satisfaction, and stickiness toward e-learning?: evidence from australia. Interactive Learning Environments, pages 1–23.
Publicado
07/04/2025
Como Citar
BORGES E SILVA, Thyago Luis; ARAÚJO, Rafael Dias; PEREIRA JÚNIOR, Cleon Xavier.
Investigação da Relação entre Traços de Personalidade e Distrações no Curso de Introdução à Programação. In: SIMPÓSIO BRASILEIRO DE EDUCAÇÃO EM COMPUTAÇÃO (EDUCOMP), 5. , 2025, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 216-227.
DOI: https://doi.org/10.5753/educomp.2025.5384.
