Investigating distractions in introductory programming courses with Learning Analytics techniques

• Thyago Luis Borges E Silva UFU
• Rafael Dias Araújo UFU
• Cleon Xavier Pereira Júnior IF Goiano

DOI: https://doi.org/10.5753/educomp_estendido.2025.6610

Abstract

Programming education faces growing challenges from digital distractions especially in introductory courses that demand logical reasoning and sustained attention. This research examines how personality traits interact with distractions by analyzing correlations between cognitive profiles and patterns of inattention. This investigation uses mixed methods to combine survey data with classroom observations and digital interaction analysis. The current findings show mainly low associations between specific personality characteristics and sources of distraction. The research has not yet identified a direct link between the measured factors and students’ academic performance. Educators will have a new visualization dashboard in the future that enables them to monitor students and evaluate their academic performance along with understanding their distraction sources.

Keywords: learning distractions, programming education, personality traits, student engagement, Learning Analytics

References

Albert, G., Fridkin, S., and Delevski, O. (2023). Mobile phone distraction and its effects on academic performance of israeli college students. Israel Affairs, 29(6):1181–1199.

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.