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Dergiler / Üniversite Araştırmaları Dergisi / 2026 / Cilt: 9 Sayı: 1

Yazılım Mühendisliği Eğitiminde Üretici Yapay Zekâ Destekli Hata Üretme ve Ayıklamaya Dayalı Öğrenimin Öğrencilerin Mesleki Öz-Yeterlilikleri Üzerindeki Etkisi

The Effect of Generative Artificial Intelligence-Supported Error Generation and Debugging-Based Learning in Software Engineering Education on Students’ Professional Self-Efficacy

Selçuk Yazar(KIRKLARELİ ÜNİVERSİTESİ)0000-0001-6567-4995
Sayfa
112–130
Yayın Tarihi
2026-03-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bulgular, yapay zekâ desteğinin mesleki öz-yeterliliği doğrudan etkilemediğini; bunun yerine öğrencilerin derinlemesine öğrenme süreçlerini tetikleyerek ve iş birliğine dayalı hata ayıklama becerilerini güçlendirerek dolaylı ve istatistiksel olarak anlamlı bir etki yarattığını ortaya koymuştur.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Günümüzde üretici yapay zekâ araçlarının yazılım mühendisliği eğitimine entegrasyonu, bu araçların öğrencileri “bilişsel tembelliğe” ittiği endişesi ile “verimliliği artıran bir asistan” olduğu görüşü arasındaki ikilem nedeniyle hızla genişleyen bir araştırma alanı haline gelmiştir.
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Temel Bulgular & Veri:Bu araştırma, Kırklareli Üniversitesi Yazılım Mühendisliği Bölümü’nde öğrenim gören (1.
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Sonuç & Katkı:Bulgular, yapay zekâ desteğinin mesleki öz-yeterliliği doğrudan etkilemediğini; bunun yerine öğrencilerin derinlemesine öğrenme süreçlerini tetikleyerek ve iş birliğine dayalı hata ayıklama becerilerini güçlendirerek dolaylı ve istatistiksel olarak anlamlı bir etki yarattığını ortaya koymuştur.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Yazılım Mühendisliği Eğitimi · Üretici Yapay Zekâ · İşbirlikçi Hata Ayıklama

Özet

Günümüzde üretici yapay zekâ araçlarının yazılım mühendisliği eğitimine entegrasyonu, bu araçların öğrencileri “bilişsel tembelliğe” ittiği endişesi ile “verimliliği artıran bir asistan” olduğu görüşü arasındaki ikilem nedeniyle hızla genişleyen bir araştırma alanı haline gelmiştir. Özellikle Büyük Dil Modellerinin ve benzeri üretici araçların kod üretimindeki başarısı, eğitimcileri, bu araçların salt çözüm üreticisi olarak değil, öğrencilerin eleştirel düşünme ve hata ayıklama becerilerini geliştirecek pedagojik bir araç olarak nasıl konumlandırılacağı sorusuna yöneltmiştir. Bu araştırma, Kırklareli Üniversitesi Yazılım Mühendisliği Bölümü’nde öğrenim gören (1. Sınıf n = 47, 2. Sınıf n = 56, 3. Sınıf n = 11 ve 4. Sınıf n = 4), lisans öğrencileri ile gerçekleştirilmiş olup, veriler ilişkisel tarama modeli esas alınarak toplanmış ve Yapısal Eşitlik Modellemesi ile analiz edilmiştir. Bulgular, yapay zekâ desteğinin mesleki öz-yeterliliği doğrudan etkilemediğini; bunun yerine öğrencilerin derinlemesine öğrenme süreçlerini tetikleyerek ve iş birliğine dayalı hata ayıklama becerilerini güçlendirerek dolaylı ve istatistiksel olarak anlamlı bir etki yarattığını ortaya koymuştur. Özellikle Önem-Performans Haritası Analizi (IPMA) ve Gerekli Koşul Analizi (NCA) sonuçları, iş birliğine dayalı hata ayıklama deneyiminin mesleki yetkinlik inancı için en stratejik öneme sahip değişken olduğunu, ancak öğrencilerin bu süreçte en düşük performansı sergilediğini göstermiştir. Çalışma sonucunda, yapay zekânın eğitim müfredatlarına entegrasyonunda odak noktasının kod üretiminden ziyade; koddaki hataların analizi, kasıtlı hata enjeksiyonu ve hata ayıklama süreçlerine kaydırılması gerektiği vurgulanmıştır.

Abstract

The integration of generative artificial intelligence tools into software engineering education has rapidly become a growing field of research due to the dilemma between the concern that these tools push students toward “cognitive laziness” and the view that they are “productivity-enhancing assistants”. The success of Large Language Models (LLMs) and similar tools in code generation in particular has prompted educators to question how these tools can be positioned not merely as solution providers but as pedagogical tools that enhance students’ critical thinking and debugging skills. This research was conducted with undergraduate students studying in the Department of Software Engineering at Kırklareli University (1st year n = 47, 2nd year n = 56, 3rd year n = 11, and 4th year n = 4). Data were collected based on a correlational survey model and analyzed using Structural Equation Modeling. The findings revealed that artificial intelligence support did not directly affect professional self-efficacy; instead, it created an indirect and statistically significant effect by triggering students’ deep learning processes and strengthening their collaborative debugging skills. Specifically, the results of the Importance-Performance Map Analysis and the Necessary Condition Analysis showed that the collaborative debugging experience was the most strategically important variable for professional competence belief, but that students performed the worst in this process. The study concluded that the focus of integrating artificial intelligence into educational curricula should shift from code production to the analysis of errors in code, intentional error injection, and debugging processes.

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Bu Makaleye Atıf Yap

Yazar, S. (2026). Yazılım Mühendisliği Eğitiminde Üretici Yapay Zekâ Destekli Hata Üretme ve Ayıklamaya Dayalı Öğrenimin Öğrencilerin Mesleki Öz-Yeterlilikleri Üzerindeki Etkisi. Üniversite Araştırmaları Dergisi, Cilt: 9 Sayı: 1, 112–130. https://doi.org/10.32329/uad.1890338

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Kaynakça (65)

  1. Aarne, O., Peltola, P., Leinonen, J., & Hellas, A. (2018). A Study of Pair Programming Enjoyment and Attendance using Study Motivation and Strategy Metrics. Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 759-764. Baltimore Maryland USA: ACM. https://doi.org/10.1145/3159450.3159493
  2. Babbie, E. R. (2013). The practice of social research. Wadsworth Cengage Learning.
  3. Becker, B. A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J., & Santos, E. A. (2023). Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, 500-506. Toronto ON Canada: ACM. https://doi.org/10.1145/3545945.3569759
  4. Büyükeke, A. (2025). Öğrencilerin Programlama Derslerinde Üretken Yapay Zekâ Araçlarını Kabulü: Genişletilmiş Teknoloji Kabul Modeli ile Türkiye’den Bulgular. Journal of University Research, 8(4), 543-556. https://doi.org/10.32329/uad.1728785
  5. Büyüköztürk, Ş., Kılıç Çakmak, E., Erkan Akgün, Ö., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri (23. bs). Ankara:Pegem Akademi Yayıncılık. https://doi.org/10.14527/9789944919289
  6. Chi, M. T. H., & Wylie, R. (2014). The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes. Educational Psychologist, 49(4), 219-243. https://doi.org/10.1080/00461520.2014.965823
  7. Cintron, L., Chang, Y., Cohoon, J., Tychonievich, L., Halsey, B., Yi, D., & Schmitt, G. (2019). Exploring Underrepresented Student Motivation and Perceptions of Collaborative Learning-Enhanced CS Undergraduate Introductory Courses. 2019 IEEE Frontiers in Education Conference (FIE), 1-6. Covington, KY, USA: IEEE. https://doi.org/10.1109/FIE43999.2019.9028463
  8. Creswell, J. W. (2019). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (Sixth edition). Saddle River, New Jersey: Pearson.
  9. Daungcharone, K. (2024). Integrating Thinking Process Model Technique with Online Collaborative Learning to Promote Programming Logical Thinking. International Journal of Information and Education Technology, 14(3), 501-509. https://doi.org/10.18178/ijiet.2024.14.3.2071
  10. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  11. De Oliveira Neto, F. G., & Dobslaw, F. (2024). Building Collaborative Learning: Exploring Social Annotation in Introductory Programming. Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training, 12-21. Lisbon Portugal: ACM. https://doi.org/10.1145/3639474.3640063
  12. Decker, A., & McGill, M. M. (2019). A Topical Review of Evaluation Instruments for Computing Education. Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 558-564. Minneapolis MN USA: ACM. https://doi.org/10.1145/3287324.3287393
  13. Deeb, F. A., & Hickey, T. (2017). Flipping introductory programming classes using spinoza and agile pedagogy. 2017 IEEE Frontiers in Education Conference (FIE), 1-9. Indianapolis, IN: IEEE. https://doi.org/10.1109/FIE.2017.8190519
  14. Deshmukh, S., & Raisinghani, V. (2019). SELF: Student Lab Framework for Improving Engagement and Learning. 2019 IEEE Tenth International Conference on Technology for Education (T4E), 234-237. Goa, India: IEEE. https://doi.org/10.1109/T4E.2019.00052
  15. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., … Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
  16. Ekici, M., & Çınar, M. (2020). Bilgisayar Programlama Öz-Yeterlik Ölçeğinin Türkçe Formunun Geçerlik ve Güvenirlik Çalışması. Anadolu Journal of Educational Sciences International, 10(2), 1017-1040. https://doi.org/10.18039/ajesi.725161
  17. Elnagar, A., & Ali, M. (2012). A modified team-based learning methodology for effective delivery of an introductory programming course. Proceedings of the 13th Annual Conference on Information Technology Education, 177-182. Calgary Alberta Canada: ACM. https://doi.org/10.1145/2380552.2380604
  18. Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2022). How to Design and Evaluate Research in Education. McGraw Hill LLC. Geliş tarihi gönderen https://books.google.com.tr/books?id=neCZzgEACAAJ
  19. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (Eighth edition). Andover, Hampshire: Cengage.
  20. Henry, T. R., Holz, H., Steinback, C., Reed, C., & Baid, A. (2004). Work in progress—Student retention and recruitment in computer science programs. 34th Annual Frontiers in Education, 2004. FIE 2004., 714-715. Savannah, GA, USA: IEEE. https://doi.org/10.1109/FIE.2004.1408622
  21. Hinton, P., McMurray, I., & Brownlow, C. (2004). SPSS Explained (0 bs). Routledge. https://doi.org/10.4324/9780203642597
  22. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  23. Ismail, N. Z., & Razak, M. R. (2024). PAIR PROGRAMMING FOR LEARNING PROGRAMMING SUBJECT IN HIGHER EDUCATION: A SYSTEMATIC REVIEW. International Journal of Education, Psychology and Counseling, 9(55), 770-789. https://doi.org/10.35631/IJEPC.955052
  24. Isong, B., Moemi, T., Dladlu, N., Motlhabane, N., Ifeoma, O., & Gasela, N. (2016). Empirical Confirmation of Pair Programming Effectiveness in the Teaching of Computer Programming. 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 276-281. Las Vegas, NV, USA: IEEE. https://doi.org/10.1109/CSCI.2016.0060
  25. Issaee, A., Motschnig, R., & Goltl, K. (2022). Learning to program in secondary classrooms: Students’ and Teachers’ perceptions of the pair-programming setting. 2022 IEEE Frontiers in Education Conference (FIE), 1-9. Uppsala, Sweden: IEEE. https://doi.org/10.1109/FIE56618.2022.9962635
  26. Karasar, N. (2024). BİLİMSEL ARAŞTIRMA YÖNTEMİ: Kavramlar İlkeler Teknikler (39. bs). Nobel Akademi.
  27. Keith, N. (2012). Managing Errors During Training. İçinde J. Bauer & C. Harteis (Ed.), Human Fallibility (ss. 173-195). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-90-481-3941-5_11
  28. Kerlinger, F. N., & Lee, H. B. (2007). Foundations of behavioral research (4. ed., [Nachdr.]). Singapore: Wadsworth.
  29. Kline, R. B. (2023). Structural Equation Modeling. İçinde A. L. Nichols & J. Edlund (Ed.), The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences (1. bs, ss. 535-558). Cambridge University Press. https://doi.org/10.1017/9781009010054.026
  30. Latulipe, C., Tadimalla, S. Y., Maher, M. L., Frevert, T. K., Mejias, M., Payton, J., … Bell, L. (2022). Developing CALI: An Inventory to Capture Collaborative Active Learning and Inclusive Practices in Introductory CS Courses. 2022 IEEE Frontiers in Education Conference (FIE), 1-9. Uppsala, Sweden: IEEE. https://doi.org/10.1109/FIE56618.2022.9962586
  31. Lohiniva, M., & Isomöttönen, V. (2025). Collaborative Methods and Study Motivation in CS1—A Systematic Literature Study. ACM Transactions on Computing Education, 25(4), 1-45. https://doi.org/10.1145/3748663
  32. Lui, D., Fields, D. A., & Kafai, Y. B. (2024). Collaborative Troubleshooting in STEM: A Case Study of High School Students Finding and Fixing Code, Circuit and Craft Challenges in Electronic Textiles. Cognition and Instruction, 42(3), 359-398. https://doi.org/10.1080/07370008.2024.2334697
  33. Mai, R., Niemand, T., & Kraus, S. (2021). A tailored-fit model evaluation strategy for better decisions about structural equation models. Technological Forecasting and Social Change, 173, 121142. https://doi.org/10.1016/j.techfore.2021.121142
  34. McGill, M. M., Decker, A., McKlin, T., & Haynie, K. (2019). A Gap Analysis of Noncognitive Constructs in Evaluation Instruments Designed for Computing Education. Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 706-712. Minneapolis MN USA: ACM. https://doi.org/10.1145/3287324.3287362
  35. Metcalfe, J. (2017). Learning from Errors. Annual Review of Psychology, 68(1), 465-489. https://doi.org/10.1146/annurev-psych-010416-044022
  36. Morales-Navarro, L., Fields, D. A., Giang, M., & Kafai, Y. B. (2023). Designing Bugs or Doing Another Project: Effects on Secondary Students’ Self-Beliefs in Computer Science. https://doi.org/10.48550/ARXIV.2305.02835
  37. Morales-Navarro, L., Fields, D. A., & Kafai, Y. B. (2024). Understanding Growth Mindset Practices in an Introductory Physical Computing Classroom: High School Students’ Engagement with Debugging by Design Activities (Versiyon 2). Versiyon 2. arXiv. https://doi.org/10.48550/ARXIV.2402.01885
  38. Morgan, M., Sinclair, J., Butler, M., Thota, N., Fraser, J., Cross, G., & Jackova, J. (2018). Understanding International Benchmarks on Student Engagement: Awareness and Research Alignment from a Computer Science Perspective. Proceedings of the 2017 ITiCSE Conference on Working Group Reports, 1-24. Bologna Italy: ACM. https://doi.org/10.1145/3174781.3174782
  39. Nagappan, N., Williams, L., Wiebe, E., Miller, C., Balik, S., Ferzli, M., & Petlick, J. (2003). Pair Learning: With an Eye Toward Future Success. İçinde F. Maurer & D. Wells (Ed.), Extreme Programming and Agile Methods—XP/Agile Universe 2003 (ss. 185-198). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-45122-8_21
  40. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
  41. Orr, J. W. (2024). Enhancing Computer Science Education with Pair Programming and Problem Solving Studios. CoED, 14(2). https://doi.org/10.18260/B259-8E-76924
  42. Pesonen, H., & Hellas, A. (2022). On Things that Matter in Learning Programming: Towards a Scale for New Programming Students. 2022 IEEE Frontiers in Education Conference (FIE), 1-9. Uppsala, Sweden: IEEE. https://doi.org/10.1109/FIE56618.2022.9962572
  43. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
  44. Prather, J., Reeves, B. N., Denny, P., Becker, B. A., Leinonen, J., Luxton-Reilly, A., … Santos, E. A. (2024). “It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers. ACM Transactions on Computer-Human Interaction, 31(1), 1-31. https://doi.org/10.1145/3617367
  45. Rahman, M. M., Paudel, R., & Sharker, M. H. (2019). Effects of Infusing Interactive and Collaborative Learning to Teach an Introductory Programming Course. 2019 IEEE Frontiers in Education Conference (FIE), 1-8. Covington, KY, USA: IEEE. https://doi.org/10.1109/FIE43999.2019.9028657
  46. Rodríguez, F. J., Kerby, N. D., & Boyer, K. E. (2013). Informing the design of a game-based learning environment for computer science: A pilot study on engagement and collaborative dialogue. The Proceeding of the 1st Workshop on AI-supported Education for Computer Science (AIEDCS 2013), 30-39.
  47. Salleh, N., Mendes, E., & Grundy, J. (2011). Empirical Studies of Pair Programming for CS/SE Teaching in Higher Education: A Systematic Literature Review. IEEE Transactions on Software Engineering, 37(4), 509-525. https://doi.org/10.1109/TSE.2010.59
  48. Savalei, V. (2021). Improving Fit Indices in Structural Equation Modeling with Categorical Data. Multivariate Behavioral Research, 56(3), 390-407. https://doi.org/10.1080/00273171.2020.1717922
  49. Srinivasan, S., & Centea, D. (2019). An Active Learning Strategy for Programming Courses. Içinde M. E. Auer & Telli, S. G., & Aydın, S. (2025). Üniversitelerde Yapay Zekânın Kullanımı: Dönüşümler, Getiriler ve Geleceğe Hazırlık. Journal of University Research, 8(1), 139-148. https://doi.org/10.32329/uad.1609305
  50. Turkaya, A., & Benli Özdemir, E. (2024). Yapay Zekâ Teknolojileri Kullanımının Ön Lisans Öğrencilerinin Dijital Okuryazarlık Düzeylerine Etkisi. Journal of University Research, 7(4), 459-472. https://doi.org/10.32329/uad.1486583
  51. T. Tsiatsos (Ed.), Mobile Technologies and Applications for the Internet of Things (ss. 327-336). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-11434-3_36
  52. S, S., & Mohanasundaram, T. (2024). Fit Indices in Structural Equation Modeling and Confirmatory Factor Analysis: Reporting Guidelines. Asian Journal of Economics, Business and Accounting, 24(7), 561–577. https://doi.org/10.9734/ajeba/2024/v24i71430
  53. Şahin, Ş., Arseven, Z., Ökmen, B., Eriş, M., & İlğan, A. (2017). İŞBİRLİKLİ ÖĞRENMEYE YÖNELİK TUTUM ÖLÇEĞİ: GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI. Kırıkkale Üniversitesi Sosyal Bilimler Dergisi, 7(1).
  54. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6. ed., international ed). Boston Munich: Pearson.
  55. Tang, X., Wong, S., Pu, K., Chen, X., Yang, Y., & Chen, Y. (2024). VizGroup: An AI-Assisted Event-Driven System for Real-Time Collaborative Programming Learning Analytics (Versiyon 2). Versiyon 2. arXiv. https://doi.org/10.48550/ARXIV.2404.08743
  56. Vygotskij, L. S., & Cole, M. (1981). Mind in society: The development of higher psychological processes (Nachdr.). Harvard Univ. Press.
  57. Wang, A. Y., Chen, Y., Chung, J. J. Y., Brooks, C., & Oney, S. (2021). PuzzleMe: Leveraging Peer Assessment for In-Class Programming Exercises. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1-24. https://doi.org/10.1145/3479559
  58. Wong, S. S. H., & Lim, S. W. H. (2022). The derring effect: Deliberate errors enhance learning. Journal of Experimental Psychology: General, 151(1), 25-40. https://doi.org/10.1037/xge0001072
  59. Wood, D., Bruner, J. S., & Ross, G. (1976). THE ROLE OF TUTORING IN PROBLEM SOLVING*. Journal of Child Psychology and Psychiatry, 17(2), 89–100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
  60. Wood, K., Parsons, D., Gasson, J., & Haden, P. (2013). It’s never too early: Pair programming in CS1. Proceedings of the Fifteenth Australasian Computing Education Conference - Volume 136, 13-21. Adelaide, Australia: Australian Computer Society, Inc.
  61. Xu, F., & Correia, A.-P. (2024). Measuring mutual engagement in the context of middle-school pair programming: Development and validation of a self-reported questionnaire. Computers in Human Behavior Reports, 14, 100415. https://doi.org/10.1016/j.chbr.2024.100415
  62. Xu, W., Wu, Y., & Ouyang, F. (2023). Multimodal learning analytics of collaborative patterns during pair programming in higher education. International Journal of Educational Technology in Higher Education, 20(1), 8. https://doi.org/10.1186/s41239-022-00377-z
  63. Yeom, S., Herbert, N., & Ryu, R. (2022). Project-Based Collaborative Learning Enhances Students’ Programming Performance. Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1, 248-254. Dublin Ireland: ACM. https://doi.org/10.1145/3502718.3524779
  64. Zhan, Z., Li, T., & Ye, Y. (2024). Effect of jigsaw‐integrated task‐driven learning on students’ motivation, computational thinking, collaborative skills, and programming performance in a high‐school programming course. Computer Applications in Engineering Education, 32(6), e22793. https://doi.org/10.1002/cae.22793
  65. Babbie, E. R. (2013). The practice of social research. Wadsworth Cengage Learning.