Bilgisayar Programlama Öz-Yeterlik Ölçeğinin Türkçe Formunun Geçerlik ve Güvenirlik Çalışması

Abstract

Bu araştırmanın amacı, Tsai, Wang ve Hsu (2019) tarafından geliştirilen Bilgisayar Programlama Öz-yeterlik Ölçeğinin Türkçe diline uyarlanması, geçerlik ve güvenirlik sonuçlarının ortaya konulmasıdır. Araştırma, 297 lise ve üniversite öğrencisi ile yürütülmüştür. Orijinal ölçek 16 madde ve beş faktörden (Mantıksal Düşünme, İşbirliği, Algoritma, Kontrol ve Hata Ayıklama) oluşmaktadır. Ölçeğin yapı geçerliğinin test edilmesi için birinci ve ikinci düzey doğrulayıcı faktör analizleri yapılmıştır. Ayrıca yakınsak ve ıraksak geçerlik çalışmaları yürütülmüştür. Güvenirlik analizi sonucunda iç tutarlılık, test yarılama ve bileşik güvenirlik katsayılarının kabul edilebilir düzeyde olduğu görülmüştür. Alt faktörlere ve ölçeğin geneline ilişkin Cronbach Alfa iç tutarlılık katsayıları: Mantıksal düşünme için .877, İşbirliği için .813, Algoritma için .775, Kontrol için .906, Hata ayıklama için .812 ve ölçeğin geneli için .911 olarak bulunmuştur. Madde analizi sonucunda düzeltilmiş madde toplam korelasyonlarının .41 ile .63 arasında değiştiği, %27’lik alt ve üst gruplar arasındaki ortalama farklarının anlamlı olduğu görülmüştür. Sonuç olarak ölçeğin Türkçe formunun lise ve üstü öğrenim kademesindeki öğrencilerin bilgisayar programlama öz-yeterliklerinin ölçülmesi amacıyla kullanılabilecek geçerli ve güvenilir bir ölçme aracı olduğu görülmüştür.

Keywords

• bilgisayar programlama
• öz-yeterlik
• bilgisayımsal düşünme

The Validity and Reliability Study of The Turkish Version of Computer Programming Self-Efficacy Scale

Abstract

The purpose of this research is to adapt the Computer Programming Self-efficacy Scale developed by Tsai, Wang and Hsu (2019) to the Turkish language and to present the validity and reliability results. The research was carried out with 297 high school and university students. The original scale consists of 16 items and five factors (Logical Thinking, Cooperation, Algorithm, Control and Debugging). First and second order confirmatory factor analysis were performed to test the construct validity of the scale. Convergent and divergent validity studies were also conducted. It was determined that the internal consistency, split-half and composite reliability coefficients were within acceptable limits. Cronbach’s alpha internal consistency coefficients for the sub-factors and overall scale were; .877 for Logical Thinking, .813 for Cooperation, .775 for Algorithm, .906 for Control, .812 for Debug, and .911 for overall scale. Item analysis showed that the corrected item total correlations ranged between .41 and .63 and the mean differences between top and bottom of 27% groups were significant. As a result, it has been determined that the Turkish form of the scale is a valid and reliable instrument that can be used to measure the computer programming self-efficacy of students at high school and higher levels.

Keywords

• computer programming
• self-efficacy
• computational thinking

References

1. Altun, A., & Mazman, S. G. (2012). Programlamaya ilişkin öz-yeterlik algısı ölçeğinin Türkçe formunun geçerlilik ve güvenirlik çalışması. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 3(2), 297-308.
2. Altun, A., & Kasalak, İ. (2018). Blok temelli programlamaya ilişkin öz-yeterlik algısı ölçeği geliştirme çalışması: Scratch örneği. Educational Technology Theory and Practice, 8(1), 209-225.
3. Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, 3. doi:10.1016/j.chb.2019.106185
4. Aşkar, P., & Davenport, D. (2009). An investigation of factors related to self-efficacy for java programming among engineering students. Turkish Online Journal of Educational Technology, 8(1), 26-32.
5. Aydın, S. (2015). An analysis of the relationship between high school students' self-efficacy, metacognitive strategy use and their academic motivation for learning biology. Journal of Education and Training Studies, 4(2), 53-59.
6. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, US: Prentice-Hall.
7. Bandura, A. (1993). Perceived Self-Efficacy in Cognitive Development and Functioning. Educational Psychologist, 28(2), 117-148. doi:10.1207/s15326985ep2802_3
8. Bandura, A. (1995). Exercise of personal and collective efficacy in changing societies. In A. Bandura (Ed.), Self-efficacy in changing societies (pp. 1-45). Cambridge, UK: Cambridge University Press.

9. Berland, M. (2016). Making, tinkering, and computational literacy. K. Peppler, E. R. Halverson, & Y. B. (Eds.). Kafai, Makeology: Makers as learners (Vol. 2, pp. 196-205). NY: Routledge.
10. Berland, M., & Lee, V. R. (2011). Collaborative strategic board games as a site for distributed computational thinking. International Journal of Game-Based Learning, 1(2), 65-81.
11. Bhardwaj, J. (2017). In search of self-efficacy: development of a new instrument for first year Computer Science students. Computer Science Education, 27(2), 79-99.
12. Büyüköztürk, Ş. (2011). Sosyal bilimler için veri analizi el kitabı. Ankara: Pegem Yayıncılık.
13. Çelik, H. E., & Yılmaz, V. (2016). LISREL 9.1 ile yapısal eşitlik modellemesi: Temel kavramlar-uygulamalar-programlama. Anı Yayıncılık.
14. Çınar, M. (2019). Nesneye Yönelik ve Robot Programlamanın Öğrenci Başarısına, Soyutlamaya, Problem Çözmeye ve Motivasyona Etkilerinin İncelenmesi. (Yayımlanmamış doktora tezi). Hacettepe Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2012). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.
15. De Vaus, D. A. (2002). Surveys in Social Research. Routledge.
16. Denning, P. J. (2009). The profession of IT Beyond computational thinking. Communications of the ACM, 52(6), 28–30. doi:10.1145/1516046.1516054
17. Diaz, E. C., & Silvain, G. L. (2020). Computational Thinking. New challenges for 21st century education. Virtualidad Educacion Y Ciencia, 11(20), 115-137.
18. diSessa, A. A. (2001). Changing minds: Computers, learning, and literacy (1st ed.). Cambridge, MA, US: The MIT Press.
19. Field A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th Edtition). SAGE Publications.
20. Feurzeig, W., & Papert, S. A. (2011). Programming-languages as a conceptual framework for teaching mathematics. Interactive Learning Environments, 19(5), 487–501.
21. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
22. Govender, D. W., & Basak, S. K. (2015). An investigation of factors related to self-efficacy for java programming among computer science education students. Journal of Governance and Regulation, 4(4), 612-619.
23. Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational researcher, 42(1), 38-43.
24. Gurer, M. D., Cetin, I., & Top, E. (2019). Factors Affecting Students' Attitudes toward Computer Programming. Informatics in Education, 18(2), 281-296. doi:10.15388/infedu.2019.13
25. Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
26. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th Edition). Boston: Cengage.
27. Hambleton, R. K., Merenda, P. F., & Spielberger, C. D. (2004). Issues, designs, and technical guidelines for adapting tests into multiple languages and cultures. In R.K. Hambleton, P. F. Marenda ve C. D. Spielberger (Eds.), Adapting educational and psychological tests for cross-cultural assessment (pp. 15-50). Psychology Press.
28. Haseski, H. İ., Ilic, U., & Tugtekin, U. (2018). Defining a New 21st Century Skill-Computational Thinking: Concepts and Trends. International Education Studies, 11(4), 29-42.
29. Haslaman, T., & Askar, P. (2007). Investigating the relationship between self-regulated learning strategies and achievement in a programming course. Hacettepe Universitesi Egitim Fakultesi Dergisi, (32), 110-122.
30. Hawlitschek, A., Koppen, V., Dietrich, A., & Zug, S. (2019). Drop-out in programming courses - prediction and prevention. Journal of Applied Research in Higher Education, 12(1), 124-136. doi:10.1108/jarhe-02-2019-0035
31. Hedrih, V. (2019). Adapting Psychological Tests and Measurement Instruments for Cross-Cultural Research: An Introduction. Routledge.
32. International Society for Technology in Education (ISTE), & Computer Science Teachers Association (CSTA). (2011). Operational Definition of Computational Thinking for K–12 Education. [Çevrim-içi: https://id.iste.org/docs/ct-documents/computational-thinking-operational-definition-flyer.pdf?sfvrsn=2], Erişim tarihi: 01.01.2020.
33. Jun, S., Han, S., Kim, H., & Lee, W. (2014). Assessing the computational literacy of elementary students on a national level in Korea. Educational Assessment Evaluation and Accountability, 26(4), 319-332. doi:10.1007/s11092-013-9185-7
34. Katai, Z. (2015). The challenge of promoting algorithmic thinking of both sciences- and humanities-oriented learners. Journal of Computer Assisted Learning, 31(4), 287-299. doi:10.1111/jcal.12070
35. Korkmaz, Ö., Kılıç, F. N., Çakır, R., & Erdoğmuş, F. U. (2019). Meslek Lisesi Bilişim Teknolojileri Öğrencilerinin Programlama Öz-Yeterlilikleri, STEM ve Bilgisayarca Düşünme Becerilerine Yönelik Algıları. Gazi Eğitim Bilimleri Dergisi, 5(2019), 196-218.
36. Korkmaz, Ö., Şahin, H., Çakır, R., & Erdoğmuş, F. U. (2019). Bilişim Teknolojileri Öğretmenlerinin Kodlamaya Dönük Tutumları, Öz-Yeterlilikleri ve Kodlama Öğretimi İçin Kullandıkları Yöntemler. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi, 38(2), 1-16. doi:10.7822/omuefd.612449
37. Kukul, V., Gökçearslan, Ş., & Günbatar, M. S. (2017). Computer programming self-efficacy scale (CPSES) for secondary school students: Development, validation and reliability. Educational Technology Theory and Practice, 7(1), 158-179.
38. Leonard, J., Buss, A., Gamboa, R., Mitchell, M., Fashola, O. S., Hubert, T., & Almughyirah, S. (2016). Using robotics and game design to enhance children’s self-efficacy, STEM attitudes, and computational thinking skills. Journal of Science Education and Technology, 25(6), 860-876. doi:10.1007/s10956-016-9628-2
39. Li, S., & Zheng, J. (2018). The Relationship Between Self-efficacy and Self-regulated Learning in One-to-One Computing Environment: The Mediated Role of Task Values. The Asia-Pacific Education Researcher, 27(6), 455-463. doi:10.1007/s40299-018-0405-2
40. Majumder, S., & Deen, M. J. (2019). Smartphone Sensors for Health Monitoring and Diagnosis. Sensors (Basel), 19(9), 1-45. doi:10.3390/s19092164
41. Mayers, A. (2013). Introduction to statistics and SPSS in psychology. Pearson Higher Ed.
42. Mazman Akar, S. G., & Altun, A. (2017). Individual differences in learning computer programming: A social cognitive approach. Contemporary Educational Technology, 8(3), 195-213.
43. Meydan, C. H., & Şeşen, H. (2011). Yapısal eşitlik modellemesi AMOS uygulamaları. Detay Yayıncılık.
44. Morrell, P. D., & Carroll, J. B. (2003). An Extended Examination of Preservice Elementary Teachers’ Science Teaching Self-Efficacy. School Science and Mathematics, 103(5), 246-251.
45. Noone, M., & Mooney, A. (2018). Visual and textual programming languages: a systematic review of the literature. Journal of Computers in Education, 5(2), 149-174.
46. Papert, S. (1980). Mindstorms: Children, Computers, And Powerful Ideas (1st ed.). NY, USA: Basic Books.
47. Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543-578. Park, I., Kim, D., Oh, J., Jang, Y., & Lim, K. (2015). Learning effects of pedagogical robots with programming in elementary school environments in Korea. Indian Journal of Science and Technology, 8(26), 1-5. doi:10.17485/ijst/2015/v8i26/80723
48. Pellas, N. (2014). The influence of computer self-efficacy, metacognitive self-regulation and self-esteem on student engagement in online learning programs: Evidence from the virtual world of Second Life. Computers in Human Behavior, (35), 157-170.
49. Peters, G. Y. (2014). The Alpha and the Omega of Scale Reliability and Validity: why and how to Abandon Cronbach’s Alpha. European Health Psychologist, 16(S), 576.
50. Ramadhan, H. A. (2000). Programming by discovery. Journal of Computer Assisted Learning, 16(1), 83-93.
51. 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.
52. Revelle, W., & Zinbarg, R. E. (2009). Coefficients Alpha, Beta, Omega, and the glb: comments on Sijtsma. Psychometrika, 74(1), 145–154.
53. Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74.
54. Schunk, D. H. (1991). Self-efficacy and academic motivation. Educational Psychologist, 26(3-4), 207-231. doi:10.1207/s15326985ep2603&4_2
55. Schunk, D. H. (2012). Learning Theories: An Educational Perspective (6th Edition). Boston, MA: Prentice Hall. Sümer, N. (2000). Yapısal Eşitlik Modelleri: Temel Kavramlar ve Örnek Uygulamalar. Türk Psikoloji Yazilari.
56. Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics (Vol. 6). Boston, MA: Pearson.
57. Tezbaşaran, A. (1996). Likert Tipi Ölçek Geliştirme Kılavuzu. Ankara: Psikologlar Derneği Yayınları.
58. Tsai, M. J., Wang, C. Y., & Hsu, P. F. (2019). Developing the computer programming self-efficacy scale for computer literacy education. Journal of Educational Computing Research, 56(8), 1345-1360.
59. Watkins, D. (1989). The role of confirmatory factor analysis in cross-cultural research. International journal of psychology, 24(6), 685-701
60. West, SG, Finch. J.F, & Curran,P.J. (1995). Structural equation models with non-normal variables: problems and remedies. In RH Hoyle (Ed.). Structural equation modeling: Concepts, issues and applications. Newbery Park, CA: Sage; p56-75.
61. Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33-35.
62. Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society, 366(1881), 3717-3725.
63. Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59, 431–449.
64. Zhong, B. C., Wang, Q. Y., Chen, J., & Li, Y. (2016). An Exploration of Three-Dimensional Integrated Assessment for Computational Thinking. Journal of Educational Computing Research, 53(4), 562-590. doi:10.1177/0735633115608444
65. Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339.
66. Zimmerman, B. J. (1990a). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3-17.
67. Zimmerman, B. J. (1990b). Self-regulating academic learning and achievement: The emergence of a social cognitive perspective. Educational Psychology Review, 2(2), 173-201. doi:10.1007/BF01322178
68. Zimmerman, B. J., Bonner, S., & Kovach, R. (1996). Developing self-regulated learners: Beyond achievement to self-efficacy. Washington, DC: American Psychological Association.
69. Zimmerman, B. J. (2011). Motivational sources and outcomes of self-regulated learning and performance. In Handbook of self-regulation of learning and performance. (pp. 49-64). New York, NY, US: Routledge.