System Engineering and Productivity

System Engineering and Productivity

The Impact of Personality Traits on the Acceptance of Block-Based Programming Languages

Document Type : Research Paper

Authors
1 M.Sc., Department of Computer Engineering, Faculty of Engineering, Shahrekord University, Shahrekord, Iran
2 Corresponding author: Assistant Professor, Department of Computer Science, Faculty of Mathematical Sciences, Shahrekord University, Shahrekord, Iran
3 Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Shahrekord University, Shahrekord, Iran
Abstract
Recent advances in information technology, especially the emergence of block-based programming languages, have revolutionized the methods of learning programming. Various factors, including users' personality traits, affect the adoption of these languages. This study presents a comprehensive model to investigate the effect of personality traits on the adoption of block-based programming languages. This model, by combining psychological theories and the technology acceptance model, analyzes the factors that affect users' perceived ease of use and usefulness. The results show that traits such as extraversion, conscientiousness, agreeableness, and gender differences have a significant impact on how users interact with these languages. Programming environments should be designed with users' personality traits in mind. The present study also suggests that future studies should examine the effects of social factors, gamification, and emerging technologies on the adoption of these languages.

Highlights

  • Findings suggest that tailoring the design of programming environments to accommodate different personality traits can enhance user adoption.
  • The research identifies potential avenues for future studies, including the influence of social factors, gamification, and emerging technologies on the acceptance of block-based programming.

Keywords
Subjects

Copyright © Elham Shakeri, Zahra Karimi, Leila Samimi-Dehkordi

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

Ampofo, I. A. S., Ampofo, I. A. J., Ampofo, B., Badzongoly, E. L. B., Boateng, F. O., & Asiedu, W. (2024). Theory of programming adoption. In Science and Information Conference (pp. 602–617). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-62269-4_39
Arpaci, I. (2021). Predicting adoption of visual programming languages: An extension of the technology acceptance model. In Recent advances in technology acceptance models and theories (pp. 41–55). Springer. https://doi.org/10.1007/978-3-030-64987-6_4
Davis, F. D., Granić, A., & Marangunić, N. (2024). The technology acceptance model: 30 years of TAM. Springer International Publishing. https://doi.org/10.1007/978-3-030-45274-2
DeLange Martinez, P., Tancredi, D. J., Pavel, M., Garcia, L., & Young, H. M. (2024). Technology acceptance among low-income Asian American older adults: Cross-sectional survey analysis. JMIR Aging, 7, Article e52498. https://doi.org/10.2196/52498
Küçük, M., Talan, T., & Demirbilek, M. (2024). The effect of creating 3D objects with block codes on spatial and computational thinking skills. Informatics in Education, 23(1), 125–143. https://doi.org/10.15388/infedu.2024.07
Listanto, V., Ramadhan, A., Firmansyah, N., & Susanti, B. H. (2023). Learners acceptance of u-KIT EDU as an educational application for robot building, coding, and controlling. Journal of Education Technology, 7(2), 279–288. https://doi.org/10.23887/jet.v7i2.58622
Martinez, P. D., Tancredi, D., Pavel, M., Garcia, L., & Young, H. M. (2025). Adapting the technology acceptance model to examine the use of information communication technologies and loneliness among low-income, older Asian Americans: Cross-sectional survey analysis. JMIR Aging, 8, Article e63856. https://doi.org/10.2196/63856
Mun, C., & Ha, H. (2022). A theoretical framework for analyzing student achievement in software education. Sustainability, 14(24), Article 16786. https://doi.org/10.3390/su142416786
Nguyen, V. T., Jung, K., & Dang, T. (2020). BlocklyAR: A visual programming interface for creating augmented reality experiences. Electronics, 9(8), Article 1205. https://doi.org/10.3390/electronics9081205
Ono, Y., Saito, D., & Washizaki, H. (2024). Evaluating preschoolers’ block programming using complexity and personality traits. In 2024 36th International Conference on Software Engineering Education and Training (CSEE&T) (pp. 1–5). IEEE. https://doi.org/10.1109/CSEET62301.2024.10663038
Papadakis, S. (2022). Can preschoolers learn computational thinking and coding skills with ScratchJr? A systematic literature review. International Journal of Educational Reform, 33(1), 160–172. https://doi.org/10.1177/10567879221076077
Phewkum, C., Kaewchaiya, J., Kobayashi, K., & Atchariyachanvanich, K. (2019). ScrambleSQL: A novel drag-and-drop SQL learning tool. In 2019 23rd International Computer Science and Engineering Conference (ICSEC) (pp. 340–344). IEEE. https://doi.org/10.1109/ICSEC47112.2019.8974815
Theodoropoulos, A., & Lepouras, G. (2022). Game design, gender and personalities in programming education. Frontiers in Computer Science, 4, Article 824995. https://doi.org/10.3389/fcomp.2022.824995
Toma, R. B. (2023). Measuring acceptance of block-based coding environments. Technology, Knowledge and Learning, 28(1), 241–251. https://doi.org/10.1007/s10758-021-09562-x
Weintrop, D., & Wilensky, U. (2017). Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education, 18(1), Article 3. https://doi.org/10.1145/3089799
Widiger, T. A., & Crego, C. (2019). The five factor model of personality structure: An update. World Psychiatry, 18(3), 271–272. https://doi.org/10.1002/wps.20658
Yusuf, A., & Yusuf Muhammad, A. (2024). Exploring clusters of novice programmers’ anxiety-induced behaviors during block- and text-based coding: A predictive and moderation analysis of programming quality and error debugging skills. Journal of Educational Computing Research, 62(7), 1798–1836. https://doi.org/10.1177/07356331241270707
Zhang, S., & Wong, G. K. (2024). Unravelling the underlying mechanism of computational thinking: The mediating role of attitudinal beliefs between personality and learning performance. Journal of Computer Assisted Learning, 40(2), 902–918. https://doi.org/10.1111/jcal.12900
Volume 5, Issue 3 - Serial Number 16
Serial No. 16, Autumn Quarterly
Autumn 2025
Pages 45-65

  • Receive Date 16 March 2025
  • Revise Date 20 April 2025
  • Accept Date 10 May 2025
  • First Publish Date 10 May 2025
  • Publish Date 22 November 2025