Bireysel Yenilikçilik ile Programlamaya Yönelik Kaygı Arasındaki İlişkinin İncelenmesi

Öz

Bu çalışma, üniversite öğrencilerinin programlama ile ilgili kaygı seviyelerini araştırmakta ve bu kaygıyı etkileyen faktörleri incelemektedir. Araştırma, öğrencilerin risk alma, liderlik nitelikleri, deneyime açıklık ve değişime direnç gibi özellikleri ile programlama kaygıları arasındaki ilişkileri incelemektedir. Çalışmanın katılımcıları, 2023 öncesinde programlama eğitimi almış olan 427 üniversite öğrencisidir. Araştırma katılımcılarının bireysel yenilikçilik ve programlama kaygıları öncelikle tanımlayıcı istatistiksel yöntemlerle analiz edilmiştir. Sonrasında, bireysel yenilikçiliğin alt boyutları ile programlama kaygı seviyeleri arasındaki ilişkiler ilişkisel tarama modelleri kullanılarak incelenmiştir. Sonuçlar, değişime karşı isteksizlik ve yeni deneyimlere açıklık ile programlama kaygısı arasında hafif bir pozitif korelasyon, risk alma isteği ve liderlik nitelikleri arasında ise mütevazı bir negatif korelasyon olduğunu göstermektedir. Bu faktörlerin kombinasyonu, öğrencilerin programlama kaygı seviyelerini orta derecede ve anlamlı bir şekilde öngörmektedir. Araştırma, eğitimciler ve program geliştiriciler için, programlama eğitimi sırasında öğrencilerin karşılaştıkları kaygıyı hafifletecek yöntemler geliştirme konusunda değerli bakış açıları sunmaktadır.

Anahtar Kelimeler

• programlama kaygısı
• değişime direnç
• fikir önderliği
• bireysel yenilikçilik
• risk alma

Investigating the Relationship Between Individual Innovativeness and Programming Anxiety

Abstract

This study investigates the levels of anxiety among university students regarding programming and examines the factors influencing this anxiety. The research explores the relationships between students’ characteristics such as, risk-taking, leadership qualities, openness to experience, and resistance to change with their programming anxiety. The study’s participants were 427 university students who had undergone programming education prior to 2023. Individual innovativeness and programming concerns of the research participants were analyzed primarily with descriptive statistical methods. Afterwards, the relationships between the sub-dimensions of individual innovativeness and levels of programming anxiety were investigated using relational screening models. Results indicate a slight positive correlation between reluctance towards change and receptiveness to new experiences with programming anxiety, whereas a modest negative correlation is observed between willingness to take risks and leadership qualities. The combination of these factors moderately and significantly predicts students’ levels of programming anxiety. The research offers valuable perspectives for educators and developers of programs seeking to create methods to ease the anxiety students face in programming education.

Keywords

• resistance to change
• risk-taking
• openness to experience
• leadership qualities
• programming anxiety

References

1. Amabile, M. T. (2018). Creativity in context: Update to the social psychology of creativity. Routledge. https://doi.org/10.4324/9780429969782
2. Ardıç, S., & Kılıçer, K. (2023). The effect of high school students’ individual innovativeness and problem solving competencies on their attitudes towards coding. Batı Anadolu Journal of Educational Sciences, 14(Special Issue 2), 1-25. https://doi.org/10.51460/baebd.1197857
3. Birdi, K., Leach, D., & Magadley, W. (2016). The relationship of individual capabilities and environmental support with different facets of designers’ innovative behavior. Journal of Product Innovation Management, 33(1), 19-35. https://doi.org/10.1111/jpim.12250
4. Bosch, N., & D’Mello, S. (2017). The affective experience of novice computer programmers. International Journal of Artificial Intelligence in Education, 27, 181-206. https://doi.org/10.1007/s40593-015-0069-5
5. Buche, M. W., Davis, L. R., & Vician, C. (2007). A longitudinal investigation of the effects of computer anxiety on performance in a computing-intensive environment. Journal of Information Systems Education, 18(4), 415.
6. Byrne, P., & Lyons, G. (2001). The effect of student attributes on success in programming. SIGCSE Bulletin, 33(3), 49–52. https://doi.org/10.1145/507758.377468
7. Chua, S. L., Chen, D. T., & Wong, A. F. L. (1999). Computer anxiety and its correlates: A meta-analysis. Computers in Human Behavior, 15(5), 609-623. https://doi.org/10.1016/S0747-5632(99)00039-4
8. Connolly, C., Murphy, E., & Moore, S. (2009). Programming anxiety amongst computing students—a key in the retention debate? IEEE Transactions on Education, 52, 52-56. https://doi.org/10.1109/TE.2008.928922

9. Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Multivariate statistics for the social sciences: SPSS and LISREL applications. Pegem Akademi.
10. Demir, F. (2022). The effect of different usage of the educational programming language in programming education on the programming anxiety and achievement. Educational Technology Research and Development, 70(3), 4171-4194. https://doi.org/10.1007/s10639-021-10750-6
11. Erdoğan, B. (2005). The relationship between programming success and academic success, general ability, attitudes towards computers, gender, and type of high school. (Master’s thesis). Marmara University, Istanbul, Turkey.
12. Erkuş, A. (2005). Scientific research spiral. Seçkin Publications.
13. Erol, O., & Kurt, A. A. (2017). Examining the attitudes of the students of the department of ITTE towards programming. Mehmet Akif Ersoy University Journal of Faculty of Education, 1(41), 314-325. https://doi.org/10.21764/efd.64721
14. Fraenkel, J. R., & Wallen, N. E. (2006). How to design and evaluate research in education. McGraw-Hill.
15. Güngör, D. (2016). Guidelines for the development and adaptation of measurement tools in psychology. Turkish Psychological Writings, 19(38), 104-112.
16. Gürsoy, K., & Çekmez, E. (2019). Investigation of middle school students’ attitudes and opinions towards programming. Turkish Journal of Computer and Mathematics Education, 10(3), 757-777. https://doi.org/10.16949/turkbilmat.466047
17. Hero, L. M., Lindfors, E., & Taatila, V. (2017). Individual innovativeness competence: A systematic review and future research agenda. International Journal of Higher Education, 6(5), 103-121. https://doi.org/10.5430/ijhe.v6n5p103
18. Housten, D. M. (1993). An exploration and analysis of the relationship among learning styles, teaching styles, gender and performance in a college computer science course. (Doctoral dissertation). Kansas State University, Manhattan, Kansas.
19. Huck, S. W. (2008). Reading statistics and research (5th ed.). Pearson.
20. Kayış, A. (2009). Reliability analysis. In Ş. Kalaycı (Ed.), SPSS applied multivariate statistical techniques (pp. 403-419). Asil Publishing.
21. Kılıçer, K., & Odabaşı, H. F. (2010). Individual Innovativeness Scale (IIS): Turkish adaptation, validity and reliability study. Hacettepe University Journal of Faculty of Education, 38(38), 150-164.
22. Kline, R. B. (2005). Principles and practice of structural equation modeling. The Guilford Press.
23. Küçüksüleymanoğlu, R., & Eğilmez, H. O. (2013). Burnout levels of music teacher candidates: The case of Uludag University. International Journal of Social Science, 6(3), 905-923. https://doi.org/10.9761/JASSS_610
24. Lau, W. W., & Yuen, A. H. (2009). Exploring the effects of gender and learning styles on computer programming performance: Implications for programming pedagogy. British Journal of Educational Technology, 40(4), 696-712. https://doi.org/10.1111/j.1467-8535.2008.00847.x
25. Necessary, J. R., & Parish, T. S. (1996). The relationships between computer usage and computer-related attitudes and behaviors. Education, 116, 384-388.
26. Pioro, B. T. (2004). Performance in an introductory computer programming course as a predictor of future success for engineering and computer science majors. Paper presented at the International Conference on Engineering Education, Gainesville.
27. Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.
28. Rogerson, C., & Scott, E. (2010). The fear factor: How it affects students learning to program in a tertiary environment. Journal of Information Technology Education: Research, 9, 147-171. https://doi.org/10.28945/1181
29. Selinger, A., & Gröstenberger, E. (2023). The effect of gender and age on computer self-efficacy, computer anxiety and perceived enjoyment among Austrian secondary school teachers. MAP Education and Humanities, 4, 1–9. https://doi.org/10.53880/2744-2373.2023.4.1
30. Yi, M. Y., Fiedler, K. D., & Park, J. S. (2006). Understanding the role of individual innovativeness in the acceptance of IT-based innovations: Comparative analyses of models and measures. Decision Sciences, 37, 393-426. https://doi.org/10.1111/j.1540-5414.2006.00132.x
31. Yildirim, O. G., & Ozdener, N. (2022). The development and validation of the Programming Anxiety Scale. International Journal of Computer Science Education in Schools, 5(3), Article n3. https://doi.org/10.21585/ijcses.v5i3.102