Personality, Distractions, and Early IDE Metrics as Predictors of Introductory Programming Performance

• Thyago L. Borges e Silva UFU
• Cleon X. Pereira Júnior IFG
• Ana Cláudia Martinez UFU
• David B. F. Oliveira UFAM
• Rafael D. Araújo UFU

DOI: https://doi.org/10.5753/educomp_estendido.2026.19822

Resumo

Introductory Programming Classes (IPC) often exhibit failure rates between 30% and 50%, motivating investigations on factors that may influence learning, not only cognitive but non-cognitive factors as well. We report an empirical study with two cohorts of Information Systems students (N=64), contrasting first-time learners (Class 1) and students repeating the course (Class 2). We combined three data sources: Big Five traits measured with Mini-IPIP, a lab-focused questionnaire on self-perceived internal/external distractions, and fine-grained CodeBench IDE logs (e.g., typing activity and successful submissions). Results indicate that associations are context-dependent: internal distractions hindered programming outcomes for novices, conscientiousness was highly correlated with academic success for Class 1, and openness to experience indicated early performance for Class 2. Across both classes, early IDE metrics were important predictors of later grades and GPA. After False Discovery Rate Correction (FDR), we found no evidence that personality moderates the distraction-performance relationship.

Referências

Brady, A. C., Kim, Y.-e., and Cutshall, J. (2021). The what, why, and how of distractions from a self-regulated learning perspective. Journal of college reading and learning, 51(2):153–172.

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.

Institute of Computing, Federal University of Amazonas (2025). CodeBench: An online judge system for computer science courses. [link]. Accessed: Nov. 06, 2025.

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 meta-analysis. 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.

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.

Pereira, F. D., Fonseca, S. C., Oliveira, E. H. T., Oliveira, D. B. F., Cristea, A. I., and Carvalho, L. S. G. (2020). Deep learning for early performance prediction of introductory programming students: a comparative and explanatory study. Revista Brasileira de Informática na Educação, 28:723–748.