Bilgisayar Programlama Derslerinde Öğrenme Motivasyonu Ölçeğinin Türkçe Uyarlaması: Geçerlilik ve Güvenilirlik Çalışması

Yıl 2018, Sayı: 1, 73 - 81, 01.04.2018

Ümmühan Avcı Halil Ersoy

Öz

Bu araştırmanın amacı Law, Lee ve Yu 2010 tarafından geliştirilen Bilgisayar Programlama Derslerinde Öğrenme Motivasyonu Ölçeği’ni Türkçe’ye uyarlamaktır. Bu amaçla geçerlik ve güvenirlik analizlerini yapmak üzere altı faktör bireysel tutum ve beklenti, zorlayıcı amaçlar, belirgin hedefler, ödül ve takdir, ceza, sosyal baskı ve rekabet ve 19 maddeden oluşan altılı Likert tipindeki ölçek, özel bir üniversitede okuyan 312 mühendislik fakültesi öğrencisine uygulanmıştır. Doğrulayıcı faktör analizi sonuçları incelendiğinde, ölçeğin altı faktörden oluştuğu doğrulanmış ve ölçeğin uyum iyiliği indislerinin yeterli düzeyde olduğu görülmüştür. Ölçeğin güvenilirliği Cronbach alfa iç tutarlılık katsayısı ile değerlendirilmiştir. Cronbach alfa katsayısının ölçeğin tamamında 0.90 olduğu ve faktörler bazında 0.71 ile 0.83 arasında değiştiği görülmüştür. Bu sonuçlar ölçeğin, bilgisayar programlama derslerinde öğrenme motivasyonunun ölçülmesinde geçerli ve güvenilir bir ölçme aracı olduğunu göstermektedir

Anahtar Kelimeler

Bilgisayar programlama dersleri, Öğrenme motivasyonu, Ölçek uyarlama, Geçerlilik, Güvenilirlik

The Adaptation of Learning Motivation in Computer Programming Courses Scale into Turkish: The Study of Validity and Reliability

Öz

The aim of this study is to adapt the Learning Motivation in Computer Programming Courses Scale developed by Law, Lee ve Yu 2010 to Turkish. In order to determine its validity and reliability, the scale consisting of 19 six-point Likert-type items within six factors individual attitude and expectation, challenging goals, clear direction, reward and recognition, punishment and social pressure and competition was applied to 312 engineering faculty students at a private university. When the results of the confirmatory factor analysis were examined, it was confirmed that the scale had six factors, and the goodness of fit indices are at an acceptable degree. The reliability of the scale was assessed by the Cronbach alpha internal consistency coefficient. The Cronbach’s alpha coefficiencies were found to be 0.90 for the whole scale and between 0.71 and 0.83 for the factors. These results show that scale is a valid and reliable measuring tool for learning motivation in computer programming courses

Anahtar Kelimeler

Computer programming courses, Learning motivation, Scale adaptation, Validity, Reliability

Kaynakça

• Ahmed, W., & Bruinsma, M. (2006). A structural model of selfconcept, autonomous motivation and academic performance in cross-cultural perspective. Electronic Journal of Research in Educational Psychology, 4(3), 551-576.
• Akpınar, Y., & Altun, A. (2014). Bilgi toplumu okullarında programlama eğitimi gereksinimi. Elementary Education Online, 13(1), 1-4.
• Altun, A., & Mazman, S. G. (2012). Programlamaya ilişkin öz yeterlilik algısı ölçeğinin türkçe formumun geçerlilik ve güvenirlik çalışması. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 3(2),297-308.
• Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness of fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173.
• Askar, P., & Davenport, D. (2009). An investigation of factors related to self-efficacy for Java programming among engineering students. TOJET: The Turkish Online Journal of Educational Technology, 8(1), 26-32.
• Bentler, P. M. (1992). On the fit of models to covariances and methodology to the Bulletin. Psychological Bulletin, 112(3), 400-404.
• Bergin, S., & Reilly, R. (2005). The influence of motivation and comfort-level on learning to program. In P. Romero, J. Good, E. Acosta-Chaparro, & S. Bryant, (Eds), Proc. PPIG 17, 293-304. Retrieved from http://eprints.maynoothuniversity.ie/8685/1/ SB-Influence-2005.pdf
• Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford Press Brown.
• Büyüköztürk, Ş. (2007). Sosyal bilimler için veri analizi elkitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum. (7. Baskı). Ankara: Pegem A Yayınları.
• Carmines, E. G., & McIver, J. P. (1981). Analyzing models with unobserved variables: Analysis of covariance structures. In G. W. Bohrnstedt, & E. F. Borgatta, (Eds.), Social measurement: Current issues (pp. 65–115). Beverly Hills, CA: Sage Publications.
• Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16, 64–73.
• Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik. SPSS ve LISREL uygulamaları. Ankara: Pegem A Yayıncılık.
• Fortier, M. S., Vallerand, R. J., & Guay, F. (1995). Academic motivation and school performance: Toward a structural model. Contemporary Educational Psychology, 20(3), 257-274.
• Grover, S., & Pea, R. (2013). Computational thinking in K–12 review of the state of the field. Educational Researcher, 42(1), 38-43.
• International Society for Technology in Education. (2016). The 2016 ISTE standards for students. Retrieved from http://www. iste.org/standards/standards/for-students-2016
• Jenkins, T. (2001). The motivation of students of programming. (A thesis for the degree of master of science). The University of Kent, Canterbury, United Kingdom. Retrieved from http:// citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.95.5098 &rep=rep1&type=pdf
• Jenkins, T. (2002). On the difficulty of learning to program. In proceedings of the 3rd annual conference of the LTSN centre for information and computer sciences (4, 53-58). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10. 1.1.596.9994&rep=rep1&type=pdf
• Johnson, R., & Wichern, D. (1998), Applied multivariate statistical analysis. Singapore: Prentice-Hall International.
• Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford Press
• Kori, K., Pedaste, M., Leijen, Ä., & Tõnisson, E. (2016). The role of programming experience in ICT students’ learning motivation and academic achievement. International Journal of Information and Education Technology, 6(5), 331-337.
• Kukul, V., Gökçearslan, Ş., & Günbatar, M. S. (2017). Computer programming self-efficiacy scale (CPSES) for secondary school students: Development, validation and reliability. Eğitim Teknolojisi Kuram ve Uygulama, 7(1), 158-179.
• Kusurkar, R. A., Ten-Cate, T. J., Vos, C. M. P., Westers, P., & Croiset, G. (2013). How motivation affects academic performance: A structural equation modelling analysis. Advances in Health Sciences Education, 18(1), 57-69.
• Kynigos, C. (1992). Insights into pupils’ and teachers’ activities in pupil-controlled problem-solving situations: A longitudinally developing use for programming by all in a primary school.
• In J. P. Ponte, et al. (Eds.), Mathematical Problem Solving and New Information Technologies. (pp. 219-238). Berlin: SpringerVerlag.
• Law, K. M. Y., Lee, V. C. S. & Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers & Education, 55, 218-228.
• Law, K. M. Y., Sandnes, F. E., Jian, H., & Huang, Y. (2009). A comparative study of learning motivation among engineering students in South East Asia and beyond. International Journal of Engineering Education, 25(1), 144–151.
• Linnenbrink, E. A., & Pintrich, P. R. (2002). Motivation as an enabler for academic success. School Psychology Review, 31(3), 313–327.
• Lynch, D. J. (2006). Motivational factors, learning strategies and resource management as predictors of course grades. College Student Journal, 40(2), 423-429.
• Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness of fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391-410.
• Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67.
• 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
• Seferoğlu, S. S., Avcı, Ü., & Kalaycı, E. (2008). Sayısal uçurum: Türkiye’deki durum ve mücadelede uygulanabilecek politikalar. 25. Ulusal Bilişim Kurultayı, 19-21.
• Serafini, G. (2011). Teaching programming at primary schools: Visions, experiences, and long-term research prospects. In International Conference on Informatics in Schools: Situation, Evolution, and Perspectives (pp. 143-154). Berlin: SpringerVerlag.
• Şimşek, Ö. F. (2007). Yapısal eşitlik modellemesine giriş. Temel ilkeler ve Lisrel uygulamaları. Ankara: Ekinoks.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Allyn & Bacon.
• TIOBE Index for January 2017. (2017). Retrieved from http://www. tiobe.com/tiobe-index/
• Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A, 366, 3717–3725. Retrieved from https://www.cs.cmu. edu/~wing/publications/Wing08a.pdf