Investigação de distrações em cursos de introdução à programação com técnicas de Learning Analytics
Resumo
As distrações digitais representam um desafio crescente no ensino de programação, especialmente em cursos introdutórios que exigem atenção e raciocínio lógico. Este estudo investiga a relação entre distrações e traços de personalidade, analisando como diferentes perfis cognitivos estão associados a padrões de dispersão. A pesquisa emprega uma abordagem de métodos mistos, integrando questionários, observações em sala de aula e análise quantitativa de interações digitais. Até o momento, os resultados indicam correlações predominantemente fracas entre certos traços de personalidade e fontes de distração. Entretanto, ainda não foi estabelecida uma relação direta com o desempenho acadêmico. No futuro, pretende-se desenvolver um painel de visualização que permita aos docentes acompanhar os alunos, compreender suas distrações e avaliar seu rendimento acadêmico de forma mais eficiente.
Palavras-chave:
distrações no aprendizado, ensino de programação, traços de personalidade, engajamento estudantil, Learning Analytics
Referências
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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.
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Creswell, J. W. and Poth, C. N. (2016). Qualitative Inquiry and Research Design: Choosing Among Five Approaches. SAGE Publications.
Flanigan, A. E. and Kiewra, K. A. (2018). What college instructors can do about student cyber-slacking. Educational Psychology Review, 30:585–597.
Jones, B. D. (2020). Motivating and engaging students using educational technologies. Handbook of research in educational communications and technology: Learning design, pages 9–35.
Luxton-Reilly, A., Simon, Albluwi, I., Becker, B. A., Giannakos, M., Kumar, A. N., Ott, L., Paterson, J., Scott, M. J., Sheard, J., et al. (2018). Introductory programming: a systematic literature review. In Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education, pages 55–106.
Masiello, I., Mohseni, Z., Palma, F., Nordmark, S., Augustsson, H., and Rundquist, R. (2024). A current overview of the use of learning analytics dashboards. Education Sciences, 14(1):82.
Mason, R. and Cooper, G. (2013). Distractions in programming environments. In Proceedings of the Fifteenth Australasian Computing Education Conference-Volume 136, pages 23–30.
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.
Mukaka, M. M. (2012). A guide to appropriate use of correlation coefficient in medical research. Malawi medical journal, 24(3):69–71.
Oliveira, J. P. (2019). Psychometric properties of the portuguese version of the mini-ipip five-factor model personality scale. Current Psychology, 38:432–439.
Packer, J. and Flack, M. (2023). The role of self-esteem, depressive symptoms, extraversion, neuroticism and fomo in problematic social media use: Exploring user profiles. International Journal of Mental Health and Addiction, pages 1–15.
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.
Pungong, C., Marakovits, S., and Lee, A. (2023a). Using goguardian teacher technology to combat students’ digital distractions on school-issued devices. European Journal of Education and Pedagogy, 4:56–59.
Pungong, C. S., Marakovits, S., and Lee, A. M. (2023b). Using goguardian teacher technology to combat students’ digital distractions on school-issued devices. European Journal of Education and Pedagogy, 4(5):56–59.
Sahin, M. and Ifenthaler, D. (2021). Visualizations and dashboards for learning analytics: A systematic literature review. Visualizations and dashboards for learning analytics, pages 3–22.
Schuett, K. C. (2024). How Do Students and Teachers Respond to Digital Distraction in School?, pages 85–94. Springer Nature Switzerland, Cham.
Sharma, S. and Behl, R. (2022). Analysing the impact of social media on students’ academic performance: a comparative study of extraversion and introversion personality. Psychological studies, 67(4):549–559.
Shibani, A., Knight, S., and Shum, S. B. (2020). Educator perspectives on learning analytics in classroom practice. The Internet and Higher Education, 46:100730.
Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Munoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Kloos, C. D., and Gašević, D. (2020). Learning analytics in european higher education—trends and barriers. Computers & Education, 155:103933.
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.
Creswell, J. W. and Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Creswell, J. W. and Poth, C. N. (2016). Qualitative Inquiry and Research Design: Choosing Among Five Approaches. SAGE Publications.
Flanigan, A. E. and Kiewra, K. A. (2018). What college instructors can do about student cyber-slacking. Educational Psychology Review, 30:585–597.
Jones, B. D. (2020). Motivating and engaging students using educational technologies. Handbook of research in educational communications and technology: Learning design, pages 9–35.
Luxton-Reilly, A., Simon, Albluwi, I., Becker, B. A., Giannakos, M., Kumar, A. N., Ott, L., Paterson, J., Scott, M. J., Sheard, J., et al. (2018). Introductory programming: a systematic literature review. In Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education, pages 55–106.
Masiello, I., Mohseni, Z., Palma, F., Nordmark, S., Augustsson, H., and Rundquist, R. (2024). A current overview of the use of learning analytics dashboards. Education Sciences, 14(1):82.
Mason, R. and Cooper, G. (2013). Distractions in programming environments. In Proceedings of the Fifteenth Australasian Computing Education Conference-Volume 136, pages 23–30.
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.
Mukaka, M. M. (2012). A guide to appropriate use of correlation coefficient in medical research. Malawi medical journal, 24(3):69–71.
Oliveira, J. P. (2019). Psychometric properties of the portuguese version of the mini-ipip five-factor model personality scale. Current Psychology, 38:432–439.
Packer, J. and Flack, M. (2023). The role of self-esteem, depressive symptoms, extraversion, neuroticism and fomo in problematic social media use: Exploring user profiles. International Journal of Mental Health and Addiction, pages 1–15.
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.
Pungong, C., Marakovits, S., and Lee, A. (2023a). Using goguardian teacher technology to combat students’ digital distractions on school-issued devices. European Journal of Education and Pedagogy, 4:56–59.
Pungong, C. S., Marakovits, S., and Lee, A. M. (2023b). Using goguardian teacher technology to combat students’ digital distractions on school-issued devices. European Journal of Education and Pedagogy, 4(5):56–59.
Sahin, M. and Ifenthaler, D. (2021). Visualizations and dashboards for learning analytics: A systematic literature review. Visualizations and dashboards for learning analytics, pages 3–22.
Schuett, K. C. (2024). How Do Students and Teachers Respond to Digital Distraction in School?, pages 85–94. Springer Nature Switzerland, Cham.
Sharma, S. and Behl, R. (2022). Analysing the impact of social media on students’ academic performance: a comparative study of extraversion and introversion personality. Psychological studies, 67(4):549–559.
Shibani, A., Knight, S., and Shum, S. B. (2020). Educator perspectives on learning analytics in classroom practice. The Internet and Higher Education, 46:100730.
Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Munoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Kloos, C. D., and Gašević, D. (2020). Learning analytics in european higher education—trends and barriers. Computers & Education, 155:103933.
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 de distrações em cursos de introdução à programação com técnicas de Learning Analytics. In: WORKSHOP DE TESES E DISSERTAÇÕES EM EDUCAÇÃO EM COMPUTAÇÃO - SIMPÓSIO BRASILEIRO DE EDUCAÇÃO EM COMPUTAÇÃO (EDUCOMP), 5. , 2025, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 73-80.
ISSN 3086-0741.
DOI: https://doi.org/10.5753/educomp_estendido.2025.6610.
