COMPUTER PROGRAMMING SELF-EFFICACY SCALE (CPSES) FOR SECONDARY SCHOOL STUDENTS: DEVELOPMENT, VALIDATION AND RELIABILITY

Abstract

Computer programming has been included in the curriculum of K12 education around the world and this has necessitated a tool for the assessment of the computer programming self-efficacy. Thus, this study aims to suggest the necessary scale for the field. In the scale development, the steps of classical measurement theory were applied. Following the expert review, the item pool was conducted with 233 students in a public secondary school which provides education to the age group of 12-14. As a result of the study, a unidimensional 5-point Likert scale of 31 items was obtained. The factor loads varied between 0.47 and 0.71 and the explained variance rate was 41.15%. In the analysis of the internal consistency, sufficient values were found; the Cronbach alpha as 0.95 and the equivalent halves method result as 0.96. For the construct validity, exploratory and confirmatory factor analysis were applied and the result showed that the scale isvalid and reliable.

References

1. Anastasiadou, S.D., & Karakos, A.S. (2011). The beliefs of electrical and computer engineering students regarding computer programming. The International Journal of Technology, Knowledge and Society, 7(1), 37-51.
2. Armoni, M. (2011). The nature of CS in K-12 curricula: the roots of confusion. ACM Inroads, 2(4), 19-20. doi:10.1145/2038876.2038883
3. 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 TOJET, 8(1): 26-32.
4. Austin, H.S. (1987). Predictors of pascal programming achievement for community college students. Proceedings of the eighteenth SIGCSE technical symposium on Computer science education, Missouri, United States, 161-164. doi: 10.1145/31726.31752
5. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215, http://dx.doi.org/10.1037/0033-295X.84.2.191
6. Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual review of psychology, 52(1), 1-26. doi:10.1146/annurev.psych.52.1.1
7. Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community?. ACM Inroads, 2(1), 48-54. doi:10.1145/1929887.1929905
8. Black, T.R. (2006). Helping novice programming students succeed. Journal of Computing Sciences in Colleges, 22(2), 109–114.

9. Booth, S. (1992). Learning to program: A phenomenographic perspective. University of Gothenburg Publication, http://hdl.handle.net/2077/16224
10. Brichacek, A. (2014). Computational thinking boosts students’ higher-order skills. Retrieved May 21, 2015 from https://www.iste.org/explore/articleDetail?articleid=232&category=Featured-videos&article=Computational%20thinking%20boosts%20students%E2%80%99%20higher-order%20skills.
11. Büyüköztürk, Ş. (2010). Sosyal bilimler için veri analizi el kitabı [Handbook of data analysis for the social sciences], Ankara: Pegem Akademi.
12. Byrne, B. M. (1998). Structural equation modeling with lisrel, prelis and simplis: basic concepts, applications, and programmings. London: Lawrence Erlbaum Assocatiates, Publishers.
13. Caspersen, M. E., & Kolling M. (2009). STREAM: A first programming process. ACT Transaction on Computing Education, 9, 1-29. doi:10.1145/1513593.1513597
14. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211, http://www.jstor.org/stable/249688
15. Çerezci, E.T. (2010). Yapısal eşitlik modelleri ve kullanılan uyum iyiliği indekslerinin karşılaştırılması. (Unpublished Doctoral Dissertation). Gazi Üniversitesi Fen Bilimleri Enstitüsü: Ankara.
16. Çokluk, Ö., Şekercioğlu, G., &Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Ankara: Pegema Yayıncılık.
17. DeVellis, R. F. (2012). Scale development: Theory and applications (Vol. 26). London: Sage publications.
18. Ellez, A. M. (2011). Ölçme araçlarında bulunması gereken özellikler. Bilimsel araştırma yöntemleri.(In Second Edition), 165-190. Ankara: Anı Yayıncılık.
19. Erdogan, Y., Aydin, E., & Kabaca, YT. (2008). Exploring the psychological predictors of programming achievement. Journal of Instructional Psychology, 35(3), 264-270.
20. 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. doi: 10.1016/j.compedu.2012.11.016
21. Fessakis, G., & Serafeim, K. (2009). Influence of the familiarization with scratch on future teachers' opinions and attitudes about programming and ICT in education. In ACM SIGCSE Bulletin (Vol. 41, No. 3, pp. 258-262). ACM. Doi: 10.1145/1595496.1562957
22. Feurzeig, W., & Papert, S. A. (2011). Programming-languages as a conceptual framework for teaching mathematics. Interactive Learning Environments, 19(5), 487-501.doi: 10.1080/10494820903520040
23. Gökçearslan, Ş., & Alper, A. (2015). The effect of locus of control on learners' sense of community and academic success in the context of online learning communities. The Internet and Higher Education, 27, 64-73. Doi: 10.1016/j.iheduc.2015.06.003
24. Grover, S., & Pea, R. (2013). Computational Thinking in K–12 A Review of the State of the Field. Educational Researcher, 42(1), 38-43. doi: 10.3102/0013189X12463051
25. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. PrenticeHall International, Upper Saddle River, New Jersey.
26. ISTE. (2007). ISTE standards students. International Society for Technology in Education: Retrieved, August, 2015 from https://www.iste.org/docs/pdfs/20-14_ISTE_Standards-S_PDF.pdf
27. Jones, S. P. (2011). Computing at School International comparisons. Retrieved Ağustos 5, 2015 from http://www.computingatschool.org.uk/index.php?id=documents adresinden.
28. Kafai, Y., & Burke, Q. (2013). Computer programming goes back to school. Phi Delta Kappan, 95(1), 61–65.
29. Kalelioğlu, F. (2015). A new way of teaching programming skills to K-12 students: Code. org. Computers in Human Behavior, 52, 200-210. doi:10.1016/j.chb.2015.05.047
30. Kan, A., & Akbaş. A. (2005). Lise öğrencilerinin kimya dersine yönelik tutum ölçeği geliştirme çalışması. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 1 (2), 227-237.
31. Kay, R. H., & Knaack, L. (2005). A case for ubiquitous, integrated computing in teacher education. Technology, Pedagogy and Education, 14(3), 391-412. doi:10.1080/14759390500200213
32. Ke, F. (2014). An implementation of design-based learning through creating educational computer games: A case study on mathematics learning during design and computing. Computers & Education, 73, 26–39. doi:10.1016/j.compedu.2013.12.010
33. Kelleher, C., & Pausch, R. (2007). Using storytelling to motivate programming. Communications of the ACM, 50(7), 58-64. Doi: 10.1145/1272516.1272540
34. Kelleher, C., Pausch, R., & Kiesler, S. (2007). Storytelling alice motivates middle school girls to learn computer programming. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 1455-1464). ACM. doi: 10.1145/1240624.1240844
35. Korkmaz, Ö., & Altun, H. (2014). Adapting computer programming self-efficacy scale and engineering students’ self-efficacy perceptions. Participatory Educational Research (PER), 1(1), 20-31, http://dx.doi.org/10.17275/per.14.02.1.1
36. Lee, J., Park, J. G., & Hwang, Y. (2013). A study on general and specific programming self-efficacy with antecedents from the social cognitive theory. Journal of Next Generation Information Technology, 4(8), 423-432.
37. Lewis, C. M. (2010). How programming environment shapes perception, learning and goals: logo vs. scratch. In Proceedings of the 41st ACM technical symposium on Computer science education (pp. 346-350). ACM. doi: 10.1145/1734263.1734383
38. 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. Doi: 10.1016/j.chb.2014.09.012
39. Maheshwari, P. (1997, July). Teaching programming paradigms and languages for qualitative learning. In Proceedings of the 2nd Australasian conference on Computer science education (pp. 32-39). ACM. doi:10.1145/299359.299365
40. Mazman, S. G., & Altun, A. (2014). Programlama-I dersinin böte bölümü öğrencilerinin programlamaya ilişkin öz yeterlilik algıları üzerine etkisi. Journal of Instructional Technologies & Teacher Education, 2(3), 24-29.
41. Murphy, C. A., Coover, D., & Owen, S. V. (1989). Development and validation of the computer self-efficacy scale. Educational and Psychological measurement, 49(4), 893-899. doi: 10.1177/001316448904900412
42. Nilsen H., & Larsen A. (2011). Using the personalized system of instruction in an introductory programming course. NOKOBIT, 27-38. November 21-23.
43. Özel, M., Timur, B., Timur, S. & Bilen, K. (2013). Öğretim elemanlarının pedagojik alan bilgilerini değerlendirme anketinin Türkçeye uyarlanması çalışması. Ahi Evran Üniversitesi Kırşehir Eğitim Fakültesi Dergisi (KEFAD), 14 (1), 407-428.
44. Pallant, J. (2010). A step by step guide to data analysis using the SPSS program. Australia: Allen and Unwin Books.
45. Phillips, P. (2009). Computational thinking a problem solving tool for every classroom. Computer Science Teacher Association. Retrieved August 2015 from http://csta.acm.org/Resources/sub/ResourceFiles/CompThinking.pdf.
46. 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. Doi: 10.2190/C670-Y3C8-LTJ1-CT3P
47. Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., ... & Kafai, Y. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60-67. doi: 10.1145/1592761.1592779
48. Sacks, C., Bellisimo, Y., & Mergendoller, J., (1993). Attitudes toward computers and computer use: the issue of gender. Journal of Research on Computing in Education, 26, 257-269. doi: 10.1080/08886504.1993.10782090
49. Seehorn, D., Carey, S., Fuschetto, B., Lee, I., Moix, D., O'Grady-Cunniff, D., ... & Verno, A. (2011). CSTA K--12 Computer Science Standards: Revised 2011. ACM.
50. Shadiev, R., Hwang, W. Y., Yeh, S. C., Yang, S. J., Wang, J. L., Han, L., & Hsu, G. L. (2014). Effects of unidirectional vs. reciprocal teaching strategies on web-based computer programming learning. Journal of Educational Computing Research, 50(1), 67-95. doi:10.2190/EC.50.1.d
51. Şimşek, Ö. F. (2007). Yapısal eşitlik modellemesine giriş, temel ilkeler ve LISREL uygulamaları. Ankara: Ekinoks Yayıncılık.
52. Tabachnick, B. G. ve Fidell, L.v S. (1996). Using multivariate statistics (3. Ed.). New York: Harper Collins College Publishers.
53. Uysal, M. P., & Yalın, H. İ. (2012). Öğretim etkinlikleri kuramına göre tasarlanan öğretim yazılımının akademik başarıya etkisi. International Journal of Human Sciences, 9(1), 185–204.
54. Weinberg, A. E. (2013). Computational thinking: An investigation of the existing scholarship and research. (Unpublished Doctoral Thesis), Colorado State University, School of Education, Colorado.
55. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
56. Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of The Royal Society, 3717-3725. doi: 10.1098/rsta.2008.0118
57. Wing, J. M. (2010). Computational thinking: What and Why? Center for Computational Thinking Carnegie Mellon: Retrieved, May 2014 Retreived from https://www.cs.cmu.edu/~CompThink/papers/TheLinkWing.pdf
58. Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82-91. doi:10.1006/ceps.1999.1016