An Empirical Investigation of Personality Traits, Self-Perceived Distractions, and Programming Performance in an Introductory Programming Class
Resumo
We investigated the interrelationships between personality traits, self-perceived distractions, and programming performance in an introductory programming class (IPC). Thirty-two undergraduate students participated in personality assessments and programming exercises on an Integrated Development Environment (IDE) platform that captured detailed behavioral interactions. We found that conscientiousness—one of the personality traits—was the strongest predictor of academic success, such as Grade Point Average (GPA). Internal distractions were significantly associated with reduced programming performance. Several IDE metrics strongly predicted academic performance, with successful submissions correlating highly with GPA. After applying False Discovery Rate correction (FDR) for multiple testing, no personality-distraction interactions remained statistically significant among the 190 moderation tests conducted, suggesting that apparent moderation effects may be attributable to chance. In our work, personality traits showed weak associations with distractions, potentially due to bias inherent in self-report measures. Our findings suggest that programming interventions should prioritize conscientiousness-based self-regulation training and internal distraction management, while some of the IDE behavioral patterns can serve as predictors for identifying at-risk students.Referências
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Eysenck, M. W. and Graydon, J. (1989). Susceptibility to distraction as a function of personality. Personality and Individual Differences, 10(6):681–687.
Flanigan, A. E. and Babchuk, W. A. (2022). Digital distraction in the classroom: exploring instructor perceptions and reactions. Teaching in Higher Education, 27(3):352–370.
Llanos, J., Bucheli, V. A., and Restrepo-Calle, F. (2023). Early prediction of student performance in cs1 programming courses. PeerJ Computer Science, 9:e1655.
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Mark, G., Czerwinski, M., and Iqbal, S. T. (2018). Effects of individual differences in blocking workplace distractions. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pages 1–12.
Mehmood, E., Abid, A., Farooq, M. S., and Nawaz, N. A. (2020). Curriculum, teaching and learning, and assessments for introductory programming course. IEEE Access, 8:125961–125981.
Oliveira, J. P. (2019). Psychometric properties of the portuguese version of the mini-ipip five-factor model personality scale. Current Psychology, 38:432–439.
Paas, F. and Van Merrienboer, J. J. (2020). Cognitive-load theory: Methods to manage working memory load in the learning of complex tasks. Current Directions in Psychological Science, 29(4):394–398.
Peitek, N., Siegmund, J., Apel, S., Kästner, C., Parnin, C., Bethmann, A., Leich, T., Saake, G., and Brechmann, A. (2018). A look into programmers’ heads. IEEE Transactions on Software Engineering, 46(4):442–462.
Pereira, F. D., Fonseca, S. C., Oliveira, E. H., Cristea, A. I., Bellhäuser, H., Rodrigues, L., Oliveira, D. B., Isotani, S., and Carvalho, L. S. (2021). Explaining individual and collective programming students’ behavior by interpreting a black-box predictive model. IEEE Access, 9:117097–117119.
Pereira, F. D., Oliveira, E. H., Oliveira, D. B., Cristea, A. I., Carvalho, L. S., Fonseca, S. C., Toda, A., and Isotani, S. (2020). Using learning analytics in the amazonas: understanding students’ behaviour in introductory programming. British journal of educational technology, 51(4):955–972.
Schober, P., Boer, C., and Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & analgesia, 126(5):1763–1768.
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.
Winkler, T. and Flatscher, R. G. (2023). Cognitive load in programming education: Easing the burden on beginners with rexx. In Central European Conference on Information and Intelligent Systems, pages 171–178. Faculty of Organization and Informatics Varazdin.
Zell, E. and Lesick, T. L. (2022). Big five personality traits and performance: A quantitative synthesis of 50+ meta-analyses. Journal of personality, 90(4):559–573.
Chandler, P. and Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and instruction, 8(4):293–332.
Coelho, F. J., Oliveira, E. H., Pereira, F. D., Oliveira, D. B., Carvalho, L. S., Souto, E. J., Pessoa, M., Melo, R., de Lima, M. A., and Nakamura, F. G. (2023). Learning analytics in introductory programming courses: a showcase from the federal university of amazonas, conforme indicado no trabalho. Revista Brasileira de Informática na Educação, 31:1089–1127.
Deng, L., Zhou, Y., and Broadbent, J. (2024). Distraction, multitasking and self-regulation inside university classroom. Education and Information Technologies, pages 1–23.
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(2):192.
Dontre, A. J. (2021). The influence of technology on academic distraction: A review. Human Behavior and Emerging Technologies, 3(3):379–390.
Esterman, M. and Rothlein, D. (2019). Models of sustained attention. Current opinion in psychology, 29:174–180.
Eysenck, M. W. and Graydon, J. (1989). Susceptibility to distraction as a function of personality. Personality and Individual Differences, 10(6):681–687.
Flanigan, A. E. and Babchuk, W. A. (2022). Digital distraction in the classroom: exploring instructor perceptions and reactions. Teaching in Higher Education, 27(3):352–370.
Llanos, J., Bucheli, V. A., and Restrepo-Calle, F. (2023). Early prediction of student performance in cs1 programming courses. PeerJ Computer Science, 9:e1655.
Mammadov, S. (2022). Big five personality traits and academic performance: A metaanalysis. Journal of personality, 90(2):222–255.
Margulieux, L. E., Morrison, B. B., and Decker, A. (2020). Reducing withdrawal and failure rates in introductory programming with subgoal labeled worked examples. International Journal of STEM Education, 7:1–16.
Mark, G., Czerwinski, M., and Iqbal, S. T. (2018). Effects of individual differences in blocking workplace distractions. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pages 1–12.
Mehmood, E., Abid, A., Farooq, M. S., and Nawaz, N. A. (2020). Curriculum, teaching and learning, and assessments for introductory programming course. IEEE Access, 8:125961–125981.
Oliveira, J. P. (2019). Psychometric properties of the portuguese version of the mini-ipip five-factor model personality scale. Current Psychology, 38:432–439.
Paas, F. and Van Merrienboer, J. J. (2020). Cognitive-load theory: Methods to manage working memory load in the learning of complex tasks. Current Directions in Psychological Science, 29(4):394–398.
Peitek, N., Siegmund, J., Apel, S., Kästner, C., Parnin, C., Bethmann, A., Leich, T., Saake, G., and Brechmann, A. (2018). A look into programmers’ heads. IEEE Transactions on Software Engineering, 46(4):442–462.
Pereira, F. D., Fonseca, S. C., Oliveira, E. H., Cristea, A. I., Bellhäuser, H., Rodrigues, L., Oliveira, D. B., Isotani, S., and Carvalho, L. S. (2021). Explaining individual and collective programming students’ behavior by interpreting a black-box predictive model. IEEE Access, 9:117097–117119.
Pereira, F. D., Oliveira, E. H., Oliveira, D. B., Cristea, A. I., Carvalho, L. S., Fonseca, S. C., Toda, A., and Isotani, S. (2020). Using learning analytics in the amazonas: understanding students’ behaviour in introductory programming. British journal of educational technology, 51(4):955–972.
Schober, P., Boer, C., and Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & analgesia, 126(5):1763–1768.
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.
Winkler, T. and Flatscher, R. G. (2023). Cognitive load in programming education: Easing the burden on beginners with rexx. In Central European Conference on Information and Intelligent Systems, pages 171–178. Faculty of Organization and Informatics Varazdin.
Zell, E. and Lesick, T. L. (2022). Big five personality traits and performance: A quantitative synthesis of 50+ meta-analyses. Journal of personality, 90(4):559–573.
Publicado
24/11/2025
Como Citar
SILVA, Thyago L. Borges e; PEREIRA JÚNIOR, Cleon X.; MARTINEZ, Ana Cláudia; OLIVEIRA, David B. F.; ARAÚJO, Rafael D..
An Empirical Investigation of Personality Traits, Self-Perceived Distractions, and Programming Performance in an Introductory Programming Class. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 36. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 509-522.
DOI: https://doi.org/10.5753/sbie.2025.12486.
