Screening Programming’s Reliability to Measure Predictive Programming Skills
Resumo
This study aimed to evaluate the reliability of an item bank developed in the Screening Programming system for measuring predictive programming skills. The results revealed that the selected items showed good content analysis and consistent psychometric properties. Furthermore, the instruments created from this item bank demonstrated good reliability in professional assessments, validating their accuracy and stability across different contexts and populations. These findings contribute to the programming field by providing a reliable instrument for assessing and developing predictive skills in this domain, fostering continuous advancements in understanding and teaching these skills.Referências
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Attallah, B., Ilagure, Z., and Chang, Y. K. (2018). The impact of competencies in mathematics and beyond on learning computer programming in higher education. In Proceedings of the Information Technology Trends (ITT). IEEE, Dubai, United Arab Emirates.
Baker, F. B. and Kim, S.-H. (2017). The basics of item response theory using R. Springer.
Barker, T. (2010). An automated feedback system based on adaptive testing: Extending the model. International Journal of Emerging Technologies in Learning (iJET), 5(2).
Cabo, C. and Lansiquot, R. D. (2014). Synergies between writing stories and writing programs in problem-solving courses. In Proceedings of the Frontiers in Education Conference (FIE). IEEE, Madrid, Spain.
Durak, H. Y. (2018). The effects of using different tools in programming teaching of secondary school students on engagement, computational thinking and reflective thinking skills for problem solving. Technology, Knowledge and Learning.
Durak, H. Y. (2020). Modeling different variables in learning basic concepts of programming in flipped classrooms. Journal of Educational Computing Research, 58(1).
Jakoš, F. and Verber, D. (2017). Learning basic programming skills with educational games: A case of primary schools in slovenia. Journal of Educational Computing Research, 55(5).
Jones, G. B. and Westhuizen, D. V. (2017). Pre-entry attributes are thought to influence the performance of students in computer programming. In Proceedings of the Southern African Computer Lecturers’ Association. Springer, Cham.
Santos, J. S., Andrade, W. L., Brunet, J., and Melo, M. R. A. (2022). A systematic literature review on predictive cognitive skills in novice programming. In 2022 IEEE Frontiers in Education Conference (FIE), pages 1–9. IEEE.
Skalka, J. and Drlík, M. (2018). Educational model for improving programming skills based on conceptual microlearning framework. In Proceedings of the International Conference on Interactive Collaborative Learning (ICL). Springer, Cham.
Smetsers, R. W. and Smetsers, S. (2017). Problem solving and algorithmic development with flowcharts. In Proceedings of the Workshop in Primary and Secondary Computing Education (WiPSCE). ACM Nijmegen, Netherlands.
Attallah, B., Ilagure, Z., and Chang, Y. K. (2018). The impact of competencies in mathematics and beyond on learning computer programming in higher education. In Proceedings of the Information Technology Trends (ITT). IEEE, Dubai, United Arab Emirates.
Baker, F. B. and Kim, S.-H. (2017). The basics of item response theory using R. Springer.
Barker, T. (2010). An automated feedback system based on adaptive testing: Extending the model. International Journal of Emerging Technologies in Learning (iJET), 5(2).
Cabo, C. and Lansiquot, R. D. (2014). Synergies between writing stories and writing programs in problem-solving courses. In Proceedings of the Frontiers in Education Conference (FIE). IEEE, Madrid, Spain.
Durak, H. Y. (2018). The effects of using different tools in programming teaching of secondary school students on engagement, computational thinking and reflective thinking skills for problem solving. Technology, Knowledge and Learning.
Durak, H. Y. (2020). Modeling different variables in learning basic concepts of programming in flipped classrooms. Journal of Educational Computing Research, 58(1).
Jakoš, F. and Verber, D. (2017). Learning basic programming skills with educational games: A case of primary schools in slovenia. Journal of Educational Computing Research, 55(5).
Jones, G. B. and Westhuizen, D. V. (2017). Pre-entry attributes are thought to influence the performance of students in computer programming. In Proceedings of the Southern African Computer Lecturers’ Association. Springer, Cham.
Santos, J. S., Andrade, W. L., Brunet, J., and Melo, M. R. A. (2022). A systematic literature review on predictive cognitive skills in novice programming. In 2022 IEEE Frontiers in Education Conference (FIE), pages 1–9. IEEE.
Skalka, J. and Drlík, M. (2018). Educational model for improving programming skills based on conceptual microlearning framework. In Proceedings of the International Conference on Interactive Collaborative Learning (ICL). Springer, Cham.
Smetsers, R. W. and Smetsers, S. (2017). Problem solving and algorithmic development with flowcharts. In Proceedings of the Workshop in Primary and Secondary Computing Education (WiPSCE). ACM Nijmegen, Netherlands.
Publicado
06/11/2023
Como Citar
DANTAS, Danilo Medeiros; SANTOS, Jucelio Soares dos; DANTAS, Kézia de Vasconcelos Oliveira; ANDRADE, Wilkerson L.; BRUNET, João; MELO, Monilly Ramos Araujo.
Screening Programming’s Reliability to Measure Predictive Programming Skills. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 34. , 2023, Passo Fundo/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 1779-1788.
DOI: https://doi.org/10.5753/sbie.2023.235112.
