Journals / Eğitim Teknolojisi Kuram ve Uygulama / 2017 / Cilt: 7 - Sayı: 1

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

Pages
158–179
Publication Date
2017-01-25
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

For the construct validity, exploratory and confirmatory factor analysis were applied and the result showed that the scale isvalid and reliable.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:For the construct validity, exploratory and confirmatory factor analysis were applied and the result showed that the scale isvalid and reliable.
🔬Design:Lab & Field Experiment
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

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.

🔖

Cite This Article

KUKUL, V., GÖKÇEARSLAN, Ş., GÜNBATAR, M. S. (2017). COMPUTER PROGRAMMING SELF-EFFICACY SCALE (CPSES) FOR SECONDARY SCHOOL STUDENTS: DEVELOPMENT, VALIDATION AND RELIABILITY. Eğitim Teknolojisi Kuram ve Uygulama, Cilt: 7 - Sayı: 1, 158–179. https://ajindex.com/articles/67930

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (9)

  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 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.
  3. 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 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
  4. 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 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 Black, T.R. (2006). Helping novice programming students succeed. Journal of Computing Sciences in Colleges, 22(2), 109–114.
  5. Booth, S. (1992). Learning to program: A phenomenographic perspective. University of Gothenburg Publication, http://hdl.handle.net/2077/16224
  6. 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.
  7. Büyüköztürk, Ş. (2010). Sosyal bilimler için veri analizi el kitabı [Handbook of data analysis for the social sciences], Ankara:
  8. Pegem Akademi. Byrne, B. M. (1998). Structural equation modeling with lisrel, prelis and simplis: basic concepts, applications, and programmings. London: Lawrence Erlbaum Assocatiates, Publishers.
  9. Caspersen, M. E., & Kolling M. (2009). STREAM: A first programming process. ACT Transaction on Computing Education, 9, 1-29. doi:10.1145/1513593.1513597 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/