Revize Edilmiş Çevrimiçi Öğrenmeye İlişkin Öğrenci Beklentileri Ölçeğinin (SEOLS-R) Türkçeye Uyarlanması

Öz

Bu çalışmanın amacı Harris, Larrier ve Castano-Bishop (2011) tarafından geliştirilen Revize Edilmiş Çevrimiçi Öğrenmeye İlişkin Öğrenci Beklentileri Ölçeği’ni (Student Expectations of Online Learning Survey Revised [SEOLS-R]) Türkçeye uyarlamaktır. Bu amaçla özgün formu 5’li Likert yapıda 7 faktör ve 43 maddeden oluşan ölçeğin uyarlanması süreci, ilk olarak gereken izinlerin alınmasıyla başlamış, daha sonra çeviri ve kültürel adaptasyon süreci gerçekleştirilmiştir. Kültürel adaptasyon sürecinin ardından Amasya Üniversitesi’nin uzaktan eğitim programlarına devam etmekte olan 411 öğrenci üzerinde ölçeğin yapı geçerliği sınanmıştır. Yapı geçerliği bulgularının ardından ölçeğin güvenirlik değerleri incelenmiştir. Elde edilen bulgular doğrultusunda, SEOLS-R ölçeğinin Türkçe formunun, Türk kültürüne uygun, geçerli ve güvenilir bir ölçme aracı olabileceği sonucuna ulaşılmıştır.

Anahtar Kelimeler

• Çevrimiçi öğrenme
• öğrenci beklentileri
• geçerlik ve güvenirlik
• ölçek uyarlama

Adaptation of the Student Expectations of Online Learning Survey Revised (SEOLS-R) into Turkish

Öz

The aim of this study is to adapt the Student Expectations of Online Learning Survey Revised [SEOLS-R] developed by Harris, Larrier and Castano-Bishop (2011) into Turkish. For this purpose, the adaptation process of the scale, which consists of 7 factors and 43 items in the 5-point Likert structure, started first with the necessary permissions, and then a translation and cultural adaptation process was carried out. After the cultural adaptation process, the construct validity of the scale was tested with 411 students who study in distance education programs of Amasya University. After the construct validity findings, the reliability values of the scale were examined. In line with the findings, it was concluded that the Turkish version of the SEOLS-R scale is a valid and reliable measurement tool suitable for Turkish culture.

Anahtar Kelimeler

• Online learning
• student expectations
• validity and reliability
• scale adaptation

Kaynakça

1. Alış, S. (2017, Nisan). Geçiş ölçeğinin Türkçe’ye uyarlanması: Geçerlilik ve güvenirlik çalışması. 3. Uluslararası Multidisipliner Avrasya Kongresi’nde sunulan bildiri, Barselona, İspanya.
2. Anık, C. (2007). Eğiticinin performansını niteleyen faktörler. Bilig, 43, 133-168.
3. Arslan, Ö. (2018). Çevrimiçi uzaktan eğitim öğrencilerinin programları terk etme nedenlerinin incelenmesi (Yayımlanmamış yüksek lisans tezi). Hacettepe Üniversitesi, Eğitim Bilimleri Enstitüsü, Ankara.
4. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.
5. Beauducel, A., & Herzberg, P. Y. (2006). On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Structural Equation Modeling: A Multidisciplinary Journal, 13(2), 186-203.
6. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-46.
7. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606.
8. Bezerra, L., & Silva, M. (2017). A review of literature on the reasons that cause the high dropout rates in the MOOCS. Revista Espacios, 38(5). Retrieved November 30, 2019 from http://www.revistaespacios.com/a17v38n05/a17v38n05p11.pdf

9. Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185-216.
10. Brown, T. A. (2015). Confirmatory factor analysis for applied research. New York: Guilford Press.
11. Büyüköztürk, Ş. (2010). Sosyal bilimler için veri analizi el kitabı (22. basım). Ankara: Pegem Akademi.
12. Cangur, S., & Ercan, I. (2015). Comparison of model fit indices used in structural equation modeling under multivariate normality. Journal of Modern Applied Statistical Methods, 14(1), 152-167.
13. Deniz, K. Z. (2007). Psikolojik ölçme aracı uyarlama. Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi, 40(1), 1-16.
14. DeVellis, R. F. (2012). Scale development: Theory and applications (3rd ed.). London: SAGE Publications.
15. Finney, S. J., & DiStefano, C. (2006). Nonnormal and categorical data in structural equation. Structural Equation Modeling: A Second Course (In G. R. Hancock & R. O. Mueller (Eds.), (pp. 269-314). Greenwich, CT: Information Age Publishing.
16. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
17. Harrington, D. (2009). Confirmatory factor analysis. Oxford: Oxford University Press.
18. Harris, S. M., Larrier, Y. I., & Castano-Bishop, M. (2011). Development of the student expectations of online learning survey (SEOLS): A pilot study. Online Journal of Distance Learning Administration, 14(4). Retrieved November 29, 2019 from https://www.westga.edu/~distance/ojdla/winter144/harris_larrier_bishop144.html
19. 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.
20. Isaac, R. G., Zerbe, W. J., & Pitt, D. C. (2001). Leadership and motivation: The effective application of expectancy theory. Journal of Managerial Issues, 13(2), 212-226.
21. Keegan, D. (1990). Foundations of distance education (2nd. ed.). New York: Routledge Publications.
22. Kelloway, E. K. (2015). Using Mplus for structural equation modeling: A researcher’s guide. Los Angeles: SAGE Publications.
23. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.
24. Laskaris, J. (2015). Why do learners drop out of a course? Retrieved November 30, 2019 from https://www.talentlms.com/blog/why-do-learners-drop-out-of-a-course/ Little, T. D. (2013). Longitudinal structural equation modeling. New York: The Guilford Press.
25. 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(3), 391-410.
26. McDonald, R. P., & Ho, M. H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64-82.
27. McDonald, R. P., & Marsh, H. W. (1990). Choosing a multivariate model: Noncentrality and goodness of fit. Psychological Bulletin, 107(2), 247-255.
28. Morgan, C. K., & Tam, M. (1999). Unraveling the complexities of distance education student attrition. Distance Education, 20(1), 96-108.
29. Muthén, L. K., & Muthén, B. O. (2011). Mplus (Version 6.12) [Computer software]. Los Angeles, CA: Muthén & Muthén.
30. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. Montréal: McGraw-Hill.
31. O'Brien, B. (2002). Online student retention: Can it be done? World Conference on Educational Multimedia, Hypermedia and Telecommunications, 2002(1), 1479-1483.
32. Okur, M. R., Paşaoğlu-Baş, D. ve Uça-Güneş, E. P. (2019). Açık ve uzaktan öğrenmede öğrenimi bırakma sebeplerinin incelenmesi. Yükseköğretim ve Bilim Dergisi, 9(2), 225-235.
33. Onah, D. F. O., Sinclair, J., & Boyatt, R. (2014, July). Dropout rates of massive open online courses: Behavioural patterns. Paper presented at the 6th International Conference on Education and New Learning Technologies, Barcelona, Spain.
34. Parker, A. (1999). A study of variables that predict dropout from distance education. International Journal of Educational Technology, 1(2), 1-12.
35. Parker, A. (2003). Identifying predictors of academic persistence in distance education. United States Distance Learning Association Journal, 17(1), 55-62.
36. Porter, L. W., & Lawler, E. E. (1968). Managerial attitudes and performance. Homewood, IL: Dorsey Press.
37. Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6(1), 1-16.
38. Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Test of significance and descriptive goodness-of-fit measures. Methods of Psychological Research - Online, 8(2), 23-74.
39. Vroom, V. H. (1964). Work and motivation. New York, NY: Wiley.
40. Willging, P. A., & Johnson, S. D. (2009). Factors that influence students' decision to dropout of online courses. Journal of Asynchronous Learning Networks, 13(3), 115-127.
41. Xenos, M. (2004). Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks. Computers & Education, 43(4), 345-359.
42. Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the course of informatics of the Hellenic Open University. Computers & Education, 39(4), 361-377.