Unlocking the Code: Exploring Predictors of Future Interest in Learning Computer Programming Among Primary School Boys and Girls

References

• Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036
   Web of Science ®Google Scholar
• Amabile, T. M. (1988). A model of creativity and innovation in organizations. Research in Organizational Behavior, 10(1), 123–167.
   Google Scholar
• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
   Web of Science ®Google Scholar
• Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., et al. (2016). A K-6 computational thinking curriculum framework- implications for teacher knowledge. Educational Technology & Society, 19(3), 47–57. http://www.jstor.org/stable/jeductechsoci.19.3.47
   Web of Science ®Google Scholar
• Arpaci, I., Durdu, P. O., & Mutlu, A. (2019). The role of self-efficacy and perceived enjoyment in predicting computer engineering students’ continuous use intention of scratch. International Journal of E-Adoption, 11(2), 1–12. https://doi.org/10.4018/IJEA.2019070101
   Web of Science ®Google Scholar
• Balanskat, A., Englehart, K. (2015). Computing our future. Computer programming and coding. Priorities, school curricula and initiatives across Europe. European Schoolnet. http://www.eun.org/documents/411753/817341/Computing+our+future_final_2015.pdf
   Google Scholar
• Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295x.84.2.191
   PubMed Web of Science ®Google Scholar
• Bandura, A. (1986). Social foundations of thought and action. Prentice-Hall, Inc.
   Google Scholar
• Beghetto, R. A. (2006). Creative self-efficacy: Correlates in middle and secondary students. Creativity Research Journal, 18(4), 447–457. https://doi.org/10.1207/s15326934crj1804_4
   Web of Science ®Google Scholar
• Boomsma, A. (1987). The robustness of maximum likelihood estimation in structural equation models. In P. Cuttance & R. Ecob (Eds.), Structural modeling by example: Applications in educational, sociological, and behavioral research (pp. 160–188). Cambridge University Press.
   Google Scholar
• Cetin, I., & Ozden, M. Y. (2015). Development of computer programming attitude scale for university students. Computer Applications in Engineering Education, 23(5), 667–672. https://doi.org/10.1002/cae.21639
   Web of Science ®Google Scholar
• Chang, C. K. (2014). Effects of using Alice and Scratch in an introductory programming course for corrective instruction. Journal of Educational Computing Research, 51(2), 185–204. https://doi.org/10.2190/EC.51.2.c
   Web of Science ®Google Scholar
• Chavatzia, T. (2017). Cracking the code: Girls’ and women’s education in science, technology, engineering and mathematics (STEM) (Vol. 253479). UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000253479
   Google Scholar
• Cheng, G. (2019). Exploring factors influencing the acceptance of visual programming environment among boys and girls in primary schools. Computers in Human Behavior, 92, 361–372. https://doi.org/10.1016/j.chb.2018.11.043
   Web of Science ®Google Scholar
• Cheng, Y. M., Lou, S. J., Kuo, S. H., & Shih, R. C. (2013). Investigating elementary school students’ technology acceptance by applying digital game-based learning to environmental education. Australasian Journal of Educational Technology, 29(1), 96–110. https://doi.org/10.14742/ajet.65
   Web of Science ®Google Scholar
• Chen, J., Perez-Felkner, L., Nhien, C., Hu, S., Erichsen, K., & Li, Y. (2023). Gender differences in motivational and curricular pathways towards postsecondary computing majors. Research in Higher Education, 1–24. Advance online publication. https://doi.org/10.1007/s11162-023-09751-w
   PubMed Web of Science ®Google Scholar
• Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175. https://doi.org/10.1016/j.compedu.2017.03.001
   Web of Science ®Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
   Google Scholar
• Conradty, C., Sotiriou, S. A., & Bogner, F. X. (2020). How creativity in STEAM modules intervenes with self-efficacy and motivation. Education Sciences, 10(3), 70. https://doi.org/10.3390/educsci10030070
   Web of Science ®Google Scholar
• Coşkunserçe, O. (2023). Comparing the use of block‐based and robot programming in introductory programming education: Effects on perceptions of programming self‐efficacy. Computer Applications in Engineering Education, 31(5), 1234–1255. https://doi.org/10.1002/cae.22637
   Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• DeCoito, I., & Briona, L. K. (2023). Fostering an entrepreneurial mindset through project-based learning and digital technologies in STEM teacher education. In Enhancing entrepreneurial mindsets through STEM education (pp. 195–222). Springer International Publishing.
   Google Scholar
• Fan, X., Miller, B. C., Park, K. E., Winward, B. W., Christensen, M., Grotevant, H. D., & Tai, R. H. (2006). An exploratory study about inaccuracy and invalidity in adolescent self-report surveys. Field Methods, 18(3), 223–244. https://doi.org/10.1177/152822X06289161
   Web of Science ®Google Scholar
• Farmer, S. M., & Tierney, P. (2017). Considering creative self-efficacy: Its current state and ideas for future inquiry. In The creative self (pp. 23–47). Academic Press.
   Google Scholar
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley.
   Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
   Web of Science ®Google Scholar
• Giannakos, M. N. (2014). Exploring students intentions to study computer science and identifying the differences among ICT and programming based courses. Turkish Online Journal of Educational Technology-TOJET, 13(3), 68–78.
   Google Scholar
• Giannakoulas, A., & Xinogalos, S. (2018). A pilot study on the effectiveness and acceptance of an educational game for teaching programming concepts to primary school students. Education and Information Technologies, 23(5), 2029–2052. https://doi.org/10.1007/s10639-018-9702-x
   Web of Science ®Google Scholar
• Grover, S., & Pea, R. (2013). Computational thinking in K-12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
   Web of Science ®Google Scholar
• Gul, D., Cetin, I., & Ozden, M. Y. (2021). A scale for measuring middle school students’ attitudes toward programming. Computer Applications in Engineering Education, 30(1), 251–258. https://doi.org/10.1002/cae.22454
   Web of Science ®Google Scholar
• Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. (7th ed.). Prentice-Hall International.
   Google Scholar
• He, W., Yan, J., Wang, C., Liao, L., & Hu, X. (2023). Exploring the impact of the design thinking model on fifth graders’ creative self-efficacy, situational interest, and individual interest in STEM education. Thinking Skills and Creativity, 50, 101424. https://doi.org/10.1016/j.tsc.2023.101424
   Web of Science ®Google Scholar
• Hoyle, R. H. (2011). Structural equation modeling for social and personality psychology. Sage.
   Google Scholar
• Hsiao, K. L., Huang, T. C., Chen, M. Y., & Chiang, N. T. (2018). Understanding the behavioral intention to play Austronesian learning games: From the perspectives of learning outcome, service quality, and hedonic value. Interactive Learning Environments, 26(3), 372–385. https://doi.org/10.1080/10494820.2017.1333011
   Web of Science ®Google Scholar
• Hsu, T. C., & Hwang, G. J. (2023). Interaction of visual interface and academic levels with young students’ anxiety, playfulness, and enjoyment in programming for robot control. Universal Access in the Information Society, 22(1), 213–225. https://doi.org/10.1007/s10209-021-00821-3
   Web of Science ®Google Scholar
• Hu, Y., Su, C. Y., & Fu, A. (2022). Factors influencing younger adolescents’ intention to use game-based programming learning: A multigroup analysis. Education and Information Technologies, 27(6), 8203–8233. https://doi.org/10.1007/s10639-022-10973-1
   Web of Science ®Google Scholar
• Huang, Y. M. (2020). Students’ continuance intention toward programming games: Hedonic and utilitarian aspects. International Journal of Human–Computer Interaction, 36(4), 393–402. https://doi.org/10.1080/10447318.2019.1647665
   Web of Science ®Google Scholar
• Huang, F., Teo, T., & Zhou, M. (2020). Chinese students’ intentions to use the internet-based technology for learning. Educational Technology Research and Development, 68(1), 575–591. https://doi.org/10.1007/s11423-019-09695-y
   Web of Science ®Google Scholar
• Hynes, B., Costin, Y., & Richardson, I. (2023). Educating for STEM: Developing entrepreneurial thinking in STEM (Entre-STEM). In S. Kaya-Capocci & E. Peters-Burton (Eds.), Enhancing entrepreneurial mindsets through STEM education. Integrated science (vol. 15). Springer. https://doi.org/10.1007/978-3-031-17816-0_8
   Google Scholar
• ISTE. (2016). ISTE standards: Students. International Society for Technology in Education. https://www.iste.org/standards/iste-standards-for-students
   Google Scholar
• Jiang, H., Chugh, R., Turnbull, D., Wang, X., & Chen, S. (2023). Modeling the impact of intrinsic coding interest on STEM career interest: Evidence from senior high school students in two large Chinese cities. Education and Information Technologies, 28(3), 2639–2659. https://doi.org/10.1007/s10639-022-11277-0
   Web of Science ®Google Scholar
• Kallia, M., & Sentance, S. (2018, October 4–6). Are boys more confident than girls? The role of calibration and students’ self-efficacy in programming tasks and computer science. WiPSCE '18: Workshop in Primary and Secondary Computing Education, Potsdam, Germany (pp. 1–4).
   Google Scholar
• Kline, R. B. (2011). Principles and practice of structural equation modelling (3rd ed.). Guilford Press.
   Google Scholar
• Kong, S. C., Chiu, M. M., & Lai, M. (2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178–189. https://doi.org/10.1016/j.compedu.2018.08.026
   Web of Science ®Google Scholar
• Lambić, D., Đorić, B., & Ivakić, S. (2021). Investigating the effect of the use of code. org on younger elementary school students’ attitudes towards programming. Behaviour & Information Technology, 40(16), 1784–1795. https://doi.org/10.1080/0144929X.2020.1781931
   Web of Science ®Google Scholar
• Lau, W. W. F., & Yuen, A. H. K. (2011). Modelling programming performance: Beyond the influence of learner characteristics. Computers & Education, 57(1), 1202–1213. https://doi.org/10.1016/j.compedu.2011.01.002
   Web of Science ®Google Scholar
• Liebenberg, J., Mentz, E., & Breed, B. (2012). Pair programming and secondary school girls’ enjoyment of programming and the subject Information Technology (IT). Computer Science Education, 22(3), 219–236. https://doi.org/10.1080/08993408.2012.713180
   Google Scholar
• Liu, X., Gu, J., & Zhao, L. (2023). Promoting primary school students’ creativity via reverse engineering pedagogy in robotics education. Thinking Skills and Creativity, 49, 101339. https://doi.org/10.1016/j.tsc.2023.101339
   Web of Science ®Google Scholar
• Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519–530. https://doi.org/10.1093/biomet/57.3.519
   Web of Science ®Google Scholar
• Mason, S. L., & Rich, P. J. (2020). Development and analysis of the elementary student coding attitudes survey. Computers & Education, 153, 103898. https://doi.org/10.1016/j.compedu.2020.103898
   Web of Science ®Google Scholar
• Master, A., Cheryan, S., & Meltzoff, A. N. (2016). Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of Educational Psychology, 108(3), 424–437. https://doi.org/10.1037/edu0000061
   Web of Science ®Google Scholar
• McDowell, C., Werner, L., Bullock, H. E., & Fernald, J. (2006). Pair programming improves student retention, confidence, and program quality. Communications of the ACM, 49(8), 90–95. https://doi.org/10.1145/1145287.1145293
   Web of Science ®Google Scholar
• McMahon, K., Ruggeri, A., Kämmer, J. E., & Katsikopoulos, K. V. (2016). Beyond idea generation: The power of groups in developing ideas. Creativity Research Journal, 28(3), 247–257. https://doi.org/10.1080/10400419.2016.1195637
   Web of Science ®Google Scholar
• Milutinović, V. (2022). Examining the influence of pre-service teachers’ digital native traits on their technology acceptance: A Serbian perspective. Education and Information Technologies, 27(5), 6483–6511. https://doi.org/10.1007/s10639-022-10887-y
   PubMed Web of Science ®Google Scholar
• Milutinović, V., & Mandić, D. (2022). Predicting teachers’ acceptance to use computers at traditional and innovative levels in teaching mathematics in Serbia/Predviđanje prihvatanja upotrebe računara na tradicionalnom i inovativnom nivou u nastavi matematike u Srbiji. Inovacije u Nastavi, 35(2), 71–88. https://doi.org/10.5937/inovacije2202071M
   Google Scholar
• Mladenović, M., Boljat, I., & Žanko, Ž. (2018). Comparing loops misconceptions in block-based and text-based programming languages at the K-12 level. Education and Information Technologies, 23(4), 1483–1500. https://doi.org/10.1007/s10639-017-9673-3
   Web of Science ®Google Scholar
• Noh, J., & Lee, J. (2020). Effects of robotics programming on the computational thinking and creativity of elementary school students. Educational Technology Research and Development, 68(1), 463–484. https://doi.org/10.1007/s11423-019-09708-w
   Web of Science ®Google Scholar
• Özyurt, Ö., & Özyurt, H. (2015). A study for determining computer programming students’ attitudes towards programming and their programming self-efficacy. Journal of Theory and Practice in Education, 11(1), 51–67. https://dergipark.org.tr/en/pub/eku/issue/5464/74179
   Google Scholar
• Papert, S. (1972). Teaching children thinking. Programmed Learning and Educational Technology, 9(5), 245–255. https://doi.org/10.1080/1355800720090503
   Google Scholar
• Paulus, P. B., & Brown, V. R. (2003). Enhancing ideational creativity in groups. In P. B. Paulus & B. A. Nijstad (Eds.), Group creativity: Innovation through collaboration (pp. 110–136). Oxford University Press.
   Google Scholar
• Popat, S., & Starkey, L. (2019). Learning to code or coding to learn? A systematic review. Computers & Education, 128, 365–376. https://doi.org/10.1016/j.compedu.2018.10.005
   Web of Science ®Google Scholar
• Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. Taylor & Francis.
   Google Scholar
• Román-González, M., Pérez-González, J. C., Moreno-León, J., & Robles, G. (2018). Extending the nomological network of computational thinking with non-cognitive factors. Computers in Human Behavior, 80, 441–459. https://doi.org/10.1016/j.chb.2017.09.030
   Web of Science ®Google Scholar
• Rubio, M. A., Romero-Zaliz, R., Mañoso, C., & Angel, P. (2015). Closing the gender gap in an introductory programming course. Computers & Education, 82, 409–420. https://doi.org/10.1016/j.compedu.2014.12.003
   Web of Science ®Google Scholar
• Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
   Web of Science ®Google Scholar
• Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
   Web of Science ®Google Scholar
• Schumacker, R. E., & Lomax, R. G. (2010). A beginner’s guide to structural equation modeling (3rd ed.). Routledge.
   Google Scholar
• Sentance, S., & Csizmadia, A. (2017). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. Education and Information Technologies, 22(2), 469–495. https://doi.org/10.1007/s10639-016-9482-0
   Web of Science ®Google Scholar
• Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual Differences, 42(5), 893–898. https://doi.org/10.1016/j.paid.2006.09.017
   Web of Science ®Google Scholar
• Sun, L., Hu, L., & Zhou, D. (2022). Programming attitudes predict computational thinking: Analysis of differences in gender and programming experience. Computers & Education, 181, 104457. https://doi.org/10.1016/j.compedu.2022.104457
   Web of Science ®Google Scholar
• Taherdoost, H. (2016). Sampling methods in research methodology; How to choose a sampling technique for research. SSRN Electronic Journal, 5, 18–27. https://doi.org/10.2139/ssrn.3205035
   Google Scholar
• Taylor, S., & Todd, P. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144
   Web of Science ®Google Scholar
• Teo, T., & Milutinovic, V. (2015). Modelling the intention to use technology for teaching Mathematics among pre-service teachers in Serbia. Australasian Journal of Educational Technology, 31(4), 363–380. https://doi.org/10.14742/ajet.1668
   Web of Science ®Google Scholar
• Teo, T., Milutinović, V., Zhou, M., & Banković, D. (2017). Traditional vs. innovative uses of computers among mathematics pre-service teachers in Serbia. Interactive Learning Environments, 25(7), 811–827. https://doi.org/10.1080/10494820.2016.1189943
   Web of Science ®Google Scholar
• Tierney, P., & Farmer, S. (2002). Creative self-efficacy: Its potential antecedents and relationship to creative performance. Academy of Management Journal, 45(6), 1137–1148. https://doi.org/10.2307/3069429
   Web of Science ®Google Scholar
• Tierney, P., & Farmer, S. M. (2011). Creative self-efficacy development and creative performance over time. The Journal of Applied Psychology, 96(2), 277–293. https://doi.org/10.1037/a0020952
   PubMed Web of Science ®Google Scholar
• Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature Review. Computers & Education, 162, 104083. https://doi.org/10.1016/j.compedu.2020.104083
   Web of Science ®Google Scholar
• Tisza, G., Markopoulos, P., & King, H. (2023). Socioeconomic background influences children’s attitudes and learning in creative programming workshop. Education and Information Technologies, 28(6), 7543–7569. https://doi.org/10.1007/s10639-022-11467-w
   Web of Science ®Google Scholar
• Tseng, H., Wang, C., Ku, H., & Sun, L. (2009). Key factors in online collaboration and their relationship to teamwork satisfaction. Quarterly Review of Distance Education, 10(2), 195–206. Retrieved August 18, 2022, from https://www.learntechlib.org/p/103641/
   Google Scholar
• Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715–728. https://doi.org/10.1007/s10639-015-9412-6
   Web of Science ®Google Scholar
• Wang, J., Hong, H., Ravitz, J., Hejazi Moghadam, S. (2016, March 2–5). Landscape of K-12 computer science education in the US: Perceptions, access, and barriers. SIGCSE '16: The 47th ACM Technical Symposium on Computing Science Education, Memphis, Tennessee, USA (pp. 645–650). https://doi.org/10.1145/2839509.2844628
   Google Scholar
• Wei, X., Lin, L., Meng, N., Tan, W., Kong, S.-C., & Kinshuk. (2021). The effectiveness of partial pair programming on elementary school students’ computational thinking skills and self-efficacy. Computers & Education, 160, 104023. https://doi.org/10.1016/j.compedu.2020.104023
   Web of Science ®Google Scholar
• 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), 1–25. https://doi.org/10.1145/3089799
   Web of Science ®Google Scholar
• Wigfield, A., & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30(1), 1–35. https://doi.org/10.1016/j.dr.2009.12.001
   Web of Science ®Google Scholar
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
   Web of Science ®Google Scholar
• World Economic Forum. (2023). The future of jobs report 20230. https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf
   Google Scholar
• Wu, B., Hu, Y., Ruis, A. R., & Wang, M. (2019). Analysing computational thinking in collaborative programming: A quantitative ethnography approach. Journal of Computer Assisted Learning, 35(3), 421–434. https://doi.org/10.1111/jcal.12348
   Web of Science ®Google Scholar
• Yildiz Durak, H. (2018). Flipped learning readiness in teaching programming in middle schools: Modelling its relation to various variables. Journal of Computer Assisted Learning, 34(6), 939–959. https://doi.org/10.1111/jcal.12302
   Web of Science ®Google Scholar
• Yukselturk, E., & Altiok, S. (2017). An investigation of the effects of programming with Scratch on the preservice IT teachers’ self‐efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789–801. https://doi.org/10.1111/bjet.12453
   Web of Science ®Google Scholar
• Zdawczyk, C., & Varma, K. (2022). Engaging girls in computer science: Gender differences in attitudes and beliefs about learning scratch and python. Computer Science Education, 33(4), 600–620. https://doi.org/10.1080/08993408.2022.2095593
   Web of Science ®Google Scholar
• Zhang, S., & Wong, G. K. W. (2024). Understanding individual differences in computational thinking development of primary school students: A three‐wave longitudinal study. Journal of Computer Assisted Learning, 1–12. Advance online publication. https://doi.org/10.1111/jcal.12940
   Web of Science ®Google Scholar
• Zhang, S., Wong, G. K., & Chan, P. C. (2023). Playing coding games to learn computational thinking: What motivates students to use this tool at home? Education and Information Technologies, 28(1), 193–216. https://doi.org/10.1007/s10639-022-11181-7
   PubMed Web of Science ®Google Scholar
• Zhang, Y., Yu, X., Cai, N., & Li, Y. (2020). Analyzing the employees’ new media use in the energy industry: The role of creative self-efficacy, perceived usefulness and leaders’ use. Sustainability, 12(3), 967. https://doi.org/10.3390/su12030967
   Web of Science ®Google Scholar