Dergiler / Kuramsal Eğitimbilim Dergisi / 2026 / Cilt: 19 Sayı: 1

Üniversite Öğrencilerinin Programlamaya İlişkin Algılanan Öğrenme Düzeylerini Yordayan Faktörlerin İncelenmesi

Examining the Factors Affecting University Students' Perceived Learning Levels in Programming

Erhan Ünal(AFYON KOCATEPE ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ, EĞİTİM BİLİMLERİ BÖLÜMÜ)0000-0002-5349-4193
Sayfa
123–145
Yayın Tarihi
2026-01-13
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Sonuç olarak 197 öğrencinin verisi ile analizler gerçekleştirilmiştir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Bu araştırmanın amacı, üniversite öğrencilerinin programlamaya yönelik tutum, motivasyon, kaygı, öz yeterlik algıları ile programlamaya ilişkin algılanan öğrenme düzeyleri arasındaki ilişkiyi belirlemek ve bu değişkenlerin programlamaya ilişkin algılanan öğrenme düzeylerini yordama düzeyini saptayabilmektir.
📊
Temel Bulgular & Veri:Araştırma korelasyonel araştırma modeline göre tasarlanmış olup 207 üniversite öğrencisinden veri toplanmıştır.
💡
Sonuç & Katkı:Sonuç olarak 197 öğrencinin verisi ile analizler gerçekleştirilmiştir.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:10 katılımcı
⚙️Değişkenler:Programlama · motivasyon · tutum

Özet

Bu araştırmanın amacı, üniversite öğrencilerinin programlamaya yönelik tutum, motivasyon, kaygı, öz yeterlik algıları ile programlamaya ilişkin algılanan öğrenme düzeyleri arasındaki ilişkiyi belirlemek ve bu değişkenlerin programlamaya ilişkin algılanan öğrenme düzeylerini yordama düzeyini saptayabilmektir. Araştırma korelasyonel araştırma modeline göre tasarlanmış olup 207 üniversite öğrencisinden veri toplanmıştır. Normallik ve çoklu regresyon analizi varsayımları kontrolü sonucunda 10 katılımcı verisi veri setinden çıkartılmıştır. Sonuç olarak 197 öğrencinin verisi ile analizler gerçekleştirilmiştir. Araştırmada veriler demografik bilgi formu, “Bilgisayar Programlama Tutum Ölçeği”, “Bilgisayar Programlama Derslerinde Öğrenme Motivasyonu Ölçeği”, “Bilgisayar Okuryazarlığı Eğitimi için Bilgisayar Programlama Öz Yeterlik Ölçeği”, “Bilgisayar Programlama Kaygı Ölçeği”, “Algılanan Öğrenme Ölçeği” kullanılarak Google Formlar aracılığıyla çevrim içi olarak toplanmıştır. Yapılan analizler sonucunda üniversite öğrencilerinin programlamaya karşı tutum, motivasyon, öz yeterlik algıları ve algılanan öğrenmelerinin yüksek düzeyde, programlamaya karşı kaygılarının ise orta düzeyde olduğu sonucuna ulaşılmıştır. Ayrıca programlamaya karşı algılanan öğrenmenin öz yeterlik algısı ile yüksek düzeyde; programlamaya karşı tutum ve motivasyon değişkenleri ile orta düzeyde pozitif ve anlamlı bir ilişkiye sahip olduğu görülürken, kaygı değişkeni ile negatif ve anlamlı bir ilişkiye sahip olduğu tespit edilmiştir. Son olarak programlamaya karşı motivasyon ve öz yeterlik algısı değişkenlerinin programlamaya karşı algılanan öğrenmeyi anlamlı bir şekilde yordadığı tespit edilmiştir.

Abstract

This study explores the relationship between university students' attitudes toward programming, their motivation, anxiety levels, self-efficacy perceptions, and how these factors influence their perceived learning in programming. A correlational research method was employed, and data were collected from 207 university students. After analysing the data for normality and checking the assumptions for multiple regression analysis, the responses of 10 participants were excluded from the dataset. As a result, the analysis was conducted using data from 197 students. Data were collected online through Google Forms, using several instruments: a demographic information form, the Computer Programming Attitude Scale, the Learning Motivation in Computer Programming Courses Scale, the Computer Programming Self-Efficacy Scale for Computer Literacy Education, the Programming Anxiety Scale, and the Perceived Learning Scale. The study reveals that university students possess a strong attitude, motivation, self-efficacy, and perceived learning in programming, while their anxiety levels are moderate. A strong positive relationship was found between students' perceived learning levels in programming and their self-efficacy perceptions. Additionally, perceived learning moderately and positively correlated with students' motivation and attitudes toward programming. Conversely, a moderate negative correlation was found between perceived learning and programming-related anxiety. Ultimately, students' perceived learning levels in programming were predicted by their self-efficacy and motivation.

🔖

Bu Makaleye Atıf Yap

Uslu, G., Ünal, E. (2026). Üniversite Öğrencilerinin Programlamaya İlişkin Algılanan Öğrenme Düzeylerini Yordayan Faktörlerin İncelenmesi. Kuramsal Eğitimbilim Dergisi, Cilt: 19 Sayı: 1, 123–145. https://doi.org/10.30831/akukeg.1757456

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (93)

  1. Abdunabi, R., Hbaci, I., & Ku, H-Y. (2019). Towards enhancing programming self-efficacy perceptions among undergraduate information systems students. Journal of Information Technology Education: Research, 18, 185-206. https://doi.org/10.28945/4308
  2. Akçay, A., & Çoklar, A.N. (2018). Investigation of perceived self-efficacy of pre-service information technology and software teachers for programming regarding different variables. Kastamonu Education Journal, 26(4), 2163-2176. https://doi.org/10.24106/kefdergi.2904
  3. Akkoyunlu, B., & Kurbanoğlu, S. (2004). A study on teachers’ information literacy self-efficacy beliefs. Hacettepe University Journal of Education, 27, 11-20. https://dergipark.org.tr/tr/download/article-file/87821
  4. Altun, A., & Mazman, S. G. (2012). Programlamaya ilişkin öz yeterlilik algısı ölçeğinin Türkçe formunun güvenirlik ve geçerlik çalışması. Journal of Measurement and Evaluation in Education and Psychology, 3(2), 297-308. https://dergipark.org.tr/en/download/article-file/65965
  5. Amnouychokanant, V., Boonlue, S., Chuathong, S., & Thamwipat, K. (2021). A study of first‐year students' attitudes toward programming in the innovation in educational technology course. Education Research International, 2021(1), 9105342. https://doi.org/10.1155/2021/9105342
  6. Askar, P., & Davenport, D. (2009). An investigation of factors related to self-efficacy for Java programming among engineering students. The Turkish Online Journal of Educational Technology, 8(1). https://eric.ed.gov/?id=ED503900
  7. Avcı, Ü. (2022). A predictive analysis of learning motivation and reflective thinking skills on computer programming achievement. Computer Applications in Engineering Education, 30(4), 1102-1116. https://doi.org/10.1002/cae.22505
  8. Avcı, Ü., & Ersoy, H. (2018). The adaptation of learning motivation in computer programming courses scale into Turkish: The study of validity and reliability. Journal of Higher Education and Science, 8(1), 73-81. https://dergipark.org.tr/tr/download/article-file/1711712
  9. Ayalew, Y., Tshukudu, E., & Lefoanea, M. (2018). Factors affecting programming performance of first year students at a University in Botswana. African Journal of Research in Mathematics, Science and Technology Education, 22(3), 363-373. https://doi.org/10.1080/18117295.2018.1540169
  10. Ayersman, D. J., & Michael Reed, W. (1995). Effects of learning styles, programming, and gender on computer anxiety. Journal of Research on Computing in Education, 28(2), 148-161. https://doi.org/10.1080/08886504.1995.10782157
  11. Bacon, D. R. (2016). Reporting actual and perceived student learning in education research. Journal of Marketing Education, 38(1), 3-6. https://doi.org/10.1177/0273475316636732
  12. Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71-81). Academic Press.
  13. Başer, M. (2013a). Attitude, gender and achievement in computer programming. Middle-East Journal of Scientific Research, 14(2), 248-255. https://doi.org/10.5829/idosi.mejsr.2013.14.2.2007
  14. Başer, M. (2013b). Developing attitude scale toward computer programming. The Journal of Academic Social Science Studies, 6(6), 199-215. http://dx.doi.org/10.9761/JASSS1702
  15. Baştemur-Kaya, C. (2018). Effect of use of Alice software on students' academic achievement, problem solving skill perceptions, motivations and readiness level to programming in computer programming teaching [Unpublished doctoral dissertation]. Gazi University.
  16. Batista, I. V., & Cornachione, E. B. (2005). Learning styles influences on satisfaction and perceived learning: Analysis of an online business game. Developments in Business Simulation and Experiential Learning, 32, 22-30. http://absel-ojs-ttu.tdl.org/absel/article/view/552
  17. Bergin, S., & Reilly, R. (2005). Programming: Factors that influence success. ACM SIGCSE Bulletin, 37(1), 411-415. https://doi.org/10.1145/1047124.1047480
  18. Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016). Developing computational thinking in compulsory education. European Commission, JRC Science for Policy Report, 68. https://publications.jrc.ec.europa.eu/repository/handle/JRC104188
  19. Breckler, S. J. (1984). Empirical validation of affect, behavior, and cognition as distinct components of attitude. Journal of Personality and Social Psychology, 47(6), 1191-1205. https://doi.org/10.1037//0022-3514.47.6.1191
  20. Büyüköztürk, Ş. (2024). Sosyal bilimler için veri analizi el kitabı: İstatistik araştırma deseni SPSS uygulamaları ve yorum (31st ed.). Pegem Akademi Yayınları.
  21. Calder, N. (2010). Using Scratch: An integrated problem-solving approach to mathematical thinking. Australian Primary Mathematics Classroom, 15(4), 9-14. https://files.eric.ed.gov/fulltext/EJ906680.pdf
  22. Caspi, A., & Blau, I. (2008). Social presence in online discussion groups: Testing three conceptions and their relations to perceived learning. Social Psychology of Education, 11(3), 323-346. https://doi.org/10.1007/s11218-008-9054-2
  23. Çetin, İ., & Özden, 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 Cigdem, H. (2015). How does self-regulation affect computer-programming achievement in a blended context?. Contemporary Educational Technology, 6(1), 19-37. https://doi.org/10.30935/cedtech/6137
  24. Çilengir, M. D., & İzmirli, S. (2023). The impact of using gamification approach in block-based programming instruction on achievement and motivation. International Journal of Computers in Education, 6(2), 79-103. https://doi.org/10.5281/ZENODO.10447397
  25. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688
  26. Connolly, C., Murphy, E., & Moore, S. (2007, September 3-7). Second chance learners, supporting adults learning computer programming. In International Conference on Engineering Education–ICEE. Coimbra, Portugal. http://icee2007.dei.uc.pt/proceedings/papers/407.pdf
  27. Computer Science Teachers Association (CSTA) & International Society for Technology in Education (ISTE) (2011). Computational thinking leadership toolkit (1st ed.). CSTA & ISTE. https://cdn.iste.org/www-root/2020-10/ISTE_CT_Leadership_Toolkit_booklet.pdf
  28. Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.
  29. Demir, F. (2015). The effect of different usage of the educational programming language in programming education on the programming anxiety and achievement [Unpublished doctoral dissertation]. Ataturk University.
  30. Demirer, V., & Sak, N. (2016). Programming education and new approaches around the world and in Turkey. Journal of Theory and Practice in Education, 12(3), 521-546. https://dergipark.org.tr/en/download/article-file/262355
  31. Durak, A., & Bulut, V. (2024). Predicting low and high student performance in programming education using PLS-SEM algorithms. Technology, Knowledge and Learning, 30, 1231-1248. https://doi.org/10.1007/s10758-024-09737-2
  32. Engin, M. (2024). Investigating the relationship between individual innovativeness and programming anxiety. Journal of University Research, 7(2), 150-159. https://doi.org/10.32329/uad.1432414
  33. Erol, O. (2015). The effects of teaching programming with Scratch on pre-service information technology teachers' motivation and achievement [Unpublished doctoral dissertation]. Anadolu University.
  34. Erol, O., & Kurt, A. A. (2017). Investigation of CEIT students’ attitudes towards programming. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, 1(41), 314-325. https://doi.org/10.21764/efd.64721
  35. European Schoolnet (2015). Computing our future: Computer programming and coding - Priorities, school curricula and initiatives across Europe http://www.eun.org/documents/411753/817341/Computing+our+future_final_2015.pdf/d3780a64-1081-4488-8549-6033200e3c03
  36. Eurydice (2022). Informatics education at school in Europe. Publications Office of the European Union. https://data.europa.eu/doi/10.2797/268406
  37. Fan, G., Liu, D., Zhang, R., & Pan, L. (2025). The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: A comparative study with traditional pair programming and individual approaches. International Journal of STEM Education, 12, Article 16. https://doi.org/10.1186/s40594-025-00537-3
  38. Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education, 63, 87-97. https://doi.org/10.1016/j.compedu.2012.11.016
  39. Fidan, A. (2016). Effect of gamification in teaching programming with scratch on student engagement [Unpublished master's thesis]. Uludağ University.
  40. Fraenkel, J., Wallen, N., & Hyun, H. (2012). How to design and evaluate research in education. McGraw-Hill Education.
  41. George, D., & Mallery, P. (2010). SPSS for Windows step by step: a simple guide and reference. Allyn & Bacon. Gökoğlu, S. (2022). Computer programming self-efficacy scale for computer literacy education: Turkish validity and reliability study. Bolu Abant Izzet Baysal University Journal of Faculty of Education, 22(2), 529-551. https://dx.doi.org/10.17240/aibuefd.2022.-654547
  42. Gomes, A., & Mendes, A. J. (2007, September 3-7). Learning to program-difficulties and solutions. In International Conference on Engineering Education–ICEE. Coimbra, Portugal. http://icee2007.dei.uc.pt/proceedings/papers/411.pdf
  43. Günbatar, M. S. (2018). Examination of undergraduate and associate degree students' computer programming attitude and self-efficacy according to thinking style, gender and experience. Contemporary Educational Technology, 9(4), 354-373. https://doi.org/10.30935/cet.471004
  44. Gürbüztürk, O., & Tanataş, D. Y. (2024). The effect of block-based coding tools on academic achievement, attitude and computational thinking skill: Meta-analysis study. Inonu University Journal of the Graduate School of Education, 11(21), 58-79. https://doi.org/10.29129/inujgse.1425193
  45. Gürer, M. D., & Tokumacı, S. (2020a). Engineering students’ attitudes towards programming. Cumhuriyet International Journal of Education, 9(4), 1064-1082. http://dx.doi.org/10.30703/cije.671244
  46. Gürer, M. D., & Tokumacı, S. (2020b). Factors affecting engineering students' achievement in computer programming. International Journal of Computer Science Education in Schools, 3(4), 23–34. https://doi.org/10.21585/ijcses.v3i4.74
  47. Gürer, M. D., Çetin, İ., & Top, E. (2019). Factors affecting students' attitudes toward computer programming. Informatics in Education, 18(2), 281-296. https://doi.org/10.15388/infedu.2019.13
  48. Hongwarittorrn, N., & Krairit, D. (2010, April). Effects of program visualization (jeliot3) on students' performance and attitudes towards java programming. In The spring 8th international conference on computing, communication and control technologies. Orlando, Florida.
  49. Horzum, M. B., & Çakır, Ö. (2009). The validity and reliability study of the Turkish version of the online technologies self-efficacy scale. Educational Sciences: Theory and Practice, 9(3), 1343-1356. https://files.eric.ed.gov/fulltext/EJ858927.pdf
  50. Horzum, M. B., Demir Kaymak, Z. & Canan Güngören, Ö. (2015). Structural equation modeling towards online learning readiness, academic motivations and perceived learning. Educational Sciences: Theory & Practice, 15(3), 759-770. https://files.eric.ed.gov/fulltext/EJ1067438.pdf
  51. ISTE. (2016). ISTE standards: Students. International Society for Technology in Education. Retrieved from https://www.iste.org/standards/iste-standards-for-students
  52. Kanaparan, G., Cullen, R., & Mason, D. (2017). Effect of self-efficacy and emotional engagement on introductory programming students. Australasian conference on ınformation systems (ACIS). Hobart, Australia. https://pdfs.semanticscholar.org/305b/845507ade4a9b412840d2acb3a8acb6c8cf6.pdf
  53. Karaci, A. (2016). Investigation of attitudes towards computer programming in terms of various variables. International Journal of Programming Languages and Applications, 6(1/2), 1-9. https://doi.org/10.5121/ijpla.2016.6201
  54. Keskinkılıç, F., & Kalelioğlu, F. (2024). An analysis of programming anxiety levels of associate degree computer department students in terms of various variables. Journal of Uludag University Faculty of Education, 37(3), 1092-1110. https://doi.org/10.19171/uefad.1493874
  55. Korkmaz, Ö., & Altun, H. (2013). Engineering and ceit student’s attitude towards learning computer programming. The Journal of Academic Social Science Studies, 6(2), 1169-1185. https://doi.org/10.9761/jasss_690
  56. Krpan, D., Mladenović, S., & Rosić, M. (2015). Undergraduate programming courses, students' perception and success. Procedia-Social and Behavioral Sciences, 174, 3868-3872. https://doi.org/10.1016/j.sbspro.2015.01.1126
  57. Lin, G.-Y. (2016). Self-efficacy beliefs and their sources in undergraduate computing disciplines: An examination of gender and persistence. Journal of Educational Computing Research, 53(4), 540-561. https://doi.org/10.1177/0735633115608440
  58. Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12?. Computers in Human Behavior, 41, 51-61. https://doi.org/10.1016/j.chb.2014.09.012
  59. Mahmud, M. M., & Wong, S. F. (2022). Digital age: The importance of 21st century skills among the undergraduates. Frontiers in Education, 7, 950553. https://doi.org/10.3389/feduc.2022.950553 Mannila, L., Peltomäki, M., & Salakoski, T. (2006). What about a simple language? Analyzing the difficulties in learning to program. Computer Science Education, 16(3), 211-227. https://doi.org/10.1080/08993400600912384
  60. Mayer, D. P. (2008). Overcoming school anxiety: How to help your child deal with separation, tests, homework, bullies, math phobia, and other worries. Amacom.
  61. Mazman, S. G., & Altun, A. (2013). The effect of introductory to programming course on programming self efficacy of CEIT students. Journal of Instructional Technologies & Teacher Education, 2(3), 24-29. https://dergipark.org.tr/tr/download/article-file/231311
  62. Mills, K. A., Cope, J., Scholes, L., & Rowe, L. (2024). Coding and computational thinking across the curriculum: A review of educational outcomes. Review of Educational Research, 95(3), 581-618. Shttps://doi.org/10.3102/00346543241241327
  63. Mutanga, M. B. (2020). The effect of cognitive factors in determining students’ success in computer programming. Journal of Theoretical and Applied Information Technology, 98(17), 3606-3618. https://www.jatit.org/volumes/Vol98No17/16Vol98No17.pdf
  64. OECD (2024), OECD Digital Economy Outlook 2024 (Volume 1): Embracing the Technology Frontier. OECD Publishing. https://doi.org/10.1787/a1689dc5-en.
  65. Olelewe, C. J., & Agomuo, E. E. (2016). Effects of B-learning and F2F learning environments on students' achievement in QBASIC programming. Computers & Education, 103, 76-86. https://doi.org/10.1016/j.compedu.2016.09.012
  66. Olipas, C. N. P., Leona, R. F., Villegas, A. C. A., Cunanan Jr, A. I., & Javate, C. L. P. (2021). The academic performance and the computer programming anxiety of BSIT students: A basis for instructional strategy improvement. International Journal of Advanced Engineering, Management and Science, 7(6), 125-129. https://doi.org/10.22161/ijaems.76.15
  67. Özmen, B., & Altun, A. (2014). Undergraduate students' experiences in programming: Difficulties and obstacles. Turkish Online Journal of Qualitative Inquiry, 5(3), 9-27. https://doi.org/10.17569/tojqi.20328
  68. Özonur, M. (2022). Computer programming students' learning motivation in programming courses. Journal of Advanced Education Studies, 4(1), 61-69. https://doi.org/10.48166/ejaes.1123170
  69. Özyurt, Ö. (2015). An analysis on distance education computer programming students' attitudes regarding programming and their self-efficacy for programming. Turkish Online Journal of Distance Education, 16(2), 111-121. https://doi.org/10.17718/tojde.58767
  70. 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://doi.org/10.17244/eku.53204
  71. Pintrich, P.R., & Schunk, D. H. (1996). Motivation in education: Theory, research, and applications. Merrill.
  72. Ramalingam, V., & Wiedenbeck, S. (1998). Development and validation of scores on a computer programming self-efficacy scale and group analyses of novice programmer self-efficacy. Journal of Educational Computing Research, 19(4), 367-381. https://doi.org/10.2190/C670-Y3C8-LTJ1-CT3P
  73. Ramalingam, V., LaBelle, D., & Wiedenbeck, S. (2004, June 28-30). Self-efficacy and mental models in learning to program. In Proceedings of the 9th Annual SIGCSE Conference on Innovation and technology in Computer Science Education (pp. 171-175). Leeds, United Kingdom. https://doi.org/10.1145/1007996.1008042
  74. Rebuta, K. M. N., Cabaron, I. M. P., Pucong, R. J. C., Bisquera, J. M. C., Llerado, R. T., & Buladaco, M. V. M. (2022). Relationship of programming skills and perceived value of learning programming among information technology education students in Davao del Sur. International Journal of Research and Innovation in Social Science (IJRISS), 6(6), 882-887. https://doi.org/10.47772/IJRISS.2022.6633
  75. Reich-Stiebert, N., Eyssel, F., & Hohnemann, C. (2019). Involve the user! Changing attitudes toward robots by user participation in a robot prototyping process. Computers in Human Behavior, 91, 290-296. https://doi.org/10.1016/j.chb.2018.09.041
  76. Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137-172. https://doi.org/10.1076/csed.13.2.137.14200
  77. 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(1), 147-171. https://doi.org/10.28945/1183
  78. Rountree, N., Rountree, J., Robins, A., & Hannah, R. (2004). Interacting factors that predict success and failure in a CS1 course. ACM SIGCSE Bulletin, 36(4), 101-104. https://doi.org/10.1145/1041624.1041669
  79. Santos, S. C., Tedesco, P. A., Borba, M., & Brito, M. (2020). Innovative approaches in teaching programming: A systematic literature review. In Proceedings of the 12th International Conference on Computer Supported Education (Vol. 1, pp. 205-214). https://doi.org/10.5220/0009190502050214.
  80. Schunk, D. H. (2012). Learning Theories an Educational Perspective. Pearson.
  81. Şişman, B., & Küçük, S. (2018). Pre-service teachers’ flow, anxiety and cognitive load levels in robotics programming. Educational Technology Theory and Practice, 8(2), 108-124. https://doi.org/10.17943/etku.366193
  82. Tokumacı, S. (2019). Factors predicting engineering faculty students' perceived learning of computer programming [Unpublished master's thesis]. Bolu Abant İzzet Baysal University.
  83. Vitasari, P., Wahab, M. N. A., Othman, A., Herawan, T., & Sinnadurai, S. K. (2010). The relationship between study anxiety and academic performance among engineering students. Procedia-Social and Behavioral Sciences, 8, 490-497. https://doi.org/10.1016/j.sbspro.2010.12.067
  84. Yağcı, M. (2016a). Effect of attitudes of information technologies (IT) preservice teachers and computer programming (CP) students toward programming on their perception regarding their self-sufficiency for programming. International Journal of Human Sciences, 13(1), 1418-1432. https://doi.org/10.14687/ijhs.v13i1.3502
  85. Yağcı, M. (2016b). Blended learning experience in a programming language course and the effect of the thinking styles of the students on success and motivation. The Turkish Online Journal of Educational Technology, 15(4), 32-45. https://files.eric.ed.gov/fulltext/EJ1117633.pdf
  86. Yıldırım, O. G. (2022). An action research study on the development of object-oriented programming course [Unpublished doctoral dissertation]. Marmara University.
  87. Yildirim, O. G., & Ozdener, N. (2021). An action research study on the development of object-oriented programming course. International Journal of Technology in Education and Science (IJTES), 5(4), 620-628. https://doi.org/10.46328/ijtes.277
  88. Yıldırım, O. G., & Özdener, N. (2022). The development and validation of the programming anxiety scale. International Journal of Computer Science Education in Schools, 5(3), 1-18. https://doi.org/10.21585/ijcses.v5i3.140
  89. Yıldız, T., & Seferoğlu, S. S. (2021). The effect of robotic programming on coding attitude and computational thinking skills toward self-efficacy perception. Journal of Learning and Teaching in Digital Age, 6(2), 101-116. https://files.eric.ed.gov/fulltext/EJ1308356.pdf
  90. Yılmaz, F., & Çakır, H. (2019). Investigation of factors affecting two-year vocational college students’ computer programming achievement. Educational Technology Theory and Practice, 9(2), 416-437. https://doi.org/10.17943/etku.527202
  91. Yılmaz, R., & Yılmaz, F. G. K. (2023). The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147
  92. Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82-91. https://doi.org/10.1006/ceps.1999.1016
  93. George, D., & Mallery, P. (2010). SPSS for Windows step by step: a simple guide and reference. Allyn & Bacon. Gökoğlu, S. (2022). Computer programming self-efficacy scale for computer literacy education: Turkish validity and reliability study. Bolu Abant Izzet Baysal University Journal of Faculty of Education, 22(2), 529-551. https://dx.doi.org/10.17240/aibuefd.2022.-654547