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
