ÜÇ ETKİLEŞİM TÜRÜNDE ÇEVRİMİÇİ ÖZ DÜZENLEME ANKETİNİN TÜRKÇEYE UYARLANMASI: GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI

Öz düzenleme, uzaktan eğitim programlarının başarıya ulaşmasında öğrenci özerkliğinin bir boyutu olarak belirleyici rol oynamaktadır. Bu bağlamda, çevrimiçi ders tasarımı için öğrenci girdilerinin ölçülmesi gerekli olduğundan, çevrimiçi uzaktan eğitim çalışmalarında öz düzenlemenin ölçülmesinin önemli olduğu düşünülmektedir. Türk dili ve kültürüne uygun üç etkileşim türünde çevrimiçi öz düzenleme için bir ölçme aracının bulunmaması göz önüne alındığında, mevcut çalışma “Üç Etkileşim Türünde Çevrimiçi Öz Düzenleme Anketi”ni Türkçe'ye uyarlamayı amaçlamaktadır. Veriler, çevrimiçi programlara kayıtlı 307 lisans ve yüksek lisans öğrencisinden toplanmıştır. Ölçme aracı 30 maddeden ve üç faktörden oluşmaktadır. Bunlar; öğrenci ve öğretmen arasındaki etkileşimde öz düzenleme, öğrenci ve öğrenci arasındaki etkileşimde öz düzenleme ve öğrenci ve içerik arasındaki etkileşimde öz düzenlemedir. Kapsam geçerliği, geliştirme çalışmasında sağlanmıştır. Aracın dil eşdeğerliği ise, geri çeviri prosedürü ile sağlanmıştır. Yapı geçerliliğini test etmek için doğrulayıcı faktör analizi yapılmıştır. İç tutarlılık, Cronbach Alpha katsayısının hesaplanmasıyla ve madde tutarlılığı düzeltilmiş madde-toplam korelasyonlarının hesaplanmasıyla sağlanmıştır. Son olarak, madde ayırt ediciliği, bağımsız örneklem t-testi yapılarak test edilmiştir. Sonuçlar, üç etkileşim türündede çevrimiçi öz düzenleme anketinin, Türkiye bağlamında çevrimiçi uzaktan eğitim ortamlarında kullanım için geçerli ve güvenilir bir araç olduğunu göstermiştir.

ADAPTATION OF THE ONLINE SELF-REGULATION QUESTIONNAIRE (OSRQ) IN THREE TYPES OF INTERACTION INTO TURKISH: A VALIDITY AND RELIABILITY STUDY

Self-Regulation is a determinant as a dimension of student autonomy on the achievement ofonline distance education programs. In this respect, measurement of self-regulation has beena crucial issue in online education studies since identification of student inputs is an essentialpart of online course or program design. Considering the unavailability of a measurementinstrument for online self-regulation in three types of interaction as appropriate with Turkishlanguage and culture, the current study aims to adapt Online Self-Regulation Questionnaire(OSRQ) into Turkish. The data were collected from 307 graduate and undergraduate studentsenrolled in fully online programs. The instrument includes 30 items and three factors; namely,Self-Regulation in interaction between student and teacher, Self-Regulation in interactionbetween student and student, and Self-Regulation in interaction between student andcontent. The content validity of the instrument was provided in its development study. Thelanguage equivalency was ensured through back-translation procedure. Confirmatory factoranalysis was conducted to test its construct validity. Internal consistency was providedthrough the calculation of Cronbach’s Alpha coefficients. Item consistency was ensured viathe calculation of the corrected item-total correlations. Finally, item discrimination was testedby performing independent samples t-test. The results indicated that OSRQ in three types ofinteraction is a valid and reliable instrument for the utilization in Turkish distance educationsettings.

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Abrami, P. C., Bernard, R. M., Bures, E. M., Borokhovski, E., & Tamim, R. M. (2011). Interaction in distance education and online learning: Using evidence and theory to improve practice. Journal of Computing in Higher Education, 23(2-3), 82–103.
Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133-148.
Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for earning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550.
Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkes, M. A., & Bethel, E. C. (2009). A meta-analysis of three types of interaction treatments in distance education. Review of Educational research, 79(3), 1243-1289.
Bol, L., & Garner, J. K. (2011). Challenges in supporting self-regulation in distance education environments. Journal of Computing in Higher Education, 23(2-3), 104-123.
Bolliger, D. U., & Halupa, C. (2018). Online student perceptions of engagement, transactional distance, and outcomes. Distance Education, 39(3), 299-316.
Broadbent, J. (2017). Comparing online and blended learner's self-regulated learning strategies and academic performance. The Internet and Higher Education, 33, 24-32.
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1-13.
Cho, M. H., & Cho, Y. (2017). Self-regulation in three types of online interaction: a scale development. Distance Education, 38(1), 70-83.
Cho, M. H., Demei, S., & Laffey, J. (2010). Relationships between self-regulation and social experiences in asynchronous online learning environments. Journal of Interactive Learning Research, 21(3), 297-316.
Cho, M. H., & Jonassen, D. (2009). Development of the human interaction dimension of the Self‐Regulated Learning Questionnaire in asynchronous online learning environments. Educational Psychology, 29(1), 117-138.
Cho, M. H., & Kim, B. J. (2013). Students' self-regulation for interaction with others in online learning environments. The Internet and Higher Education, 17, 69-75.
Cho, M. H., Kim, Y., & Choi, D. (2017). The effect of self-regulated learning on college students' perceptions of community of inquiry and affective outcomes in online learning. The Internet and Higher Education, 34, 10-17.
Cho, M. H., & Shen, D. (2013). Self-regulation in online learning. Distance Education, 34(3), 290-301.
Dancy, C. P., & Reidy, J. (2002). Statistics without maths for psychology: Using SPSS for Windows. Pearson: Prentice Hall.
Ekwunife-Orakwue, K. C., & Teng, T. L. (2014). The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments. Computers & Education, 78, 414-427.
Field, A. P. (2009). Discovering statistics using SPSS (3rd ed.). Los Angeles, CA: Sage.
Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). New York: McGraw-Hill.
Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
Horzum, M. B. (2015). Interaction, Structure, Social Presence, and Satisfaction in Online Learning. Eurasia Journal of Mathematics, Science & Technology Education, 11(3), 505- 512.
Hu, L. T., & 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.
Kuo, Y. C., Walker, A. E., Schroder, K. E., & Belland, B. R. (2014). Interaction, Internet selfefficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35-50.
Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1-7.
Moore, M. G. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical principles of distance education. New York: Routledge.
Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Belmont, CA: Cengage Learning.
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430-445.
Paul, R. C., Swart, W., Zhang, A. M., & MacLeod, K. R. (2015). Revisiting Zhang’s scale of transactional distance: Refinement and validation using structural equation modeling. Distance Education, 36(3), 364-382.
Pintrich, P.R., ve De Groot, E. (1990). Motivational and self regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40.
Shea, J., Joaquin, M. E., & Wang, J. Q. (2016). Pedagogical design factors that enhance learning in hybrid courses: A contribution to design-based instructional theory. Journal of Public Affairs Education, 22(3), 381-397.
Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual differences, 42(5), 893-898.
Stevens, J. P. (2012). Applied multivariate statistics for the social sciences. New York (N.Y.): Routledge.
Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self‐efficacy and self‐ regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191-204.
Swart, W., MacLeod, K., Paul, R., Zhang, A., & Gagulic, M. (2014). Relative proximity theory: Measuring the gap between actual and ideal online course delivery. American Journal of Distance Education, 28(4), 222-240.
Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8(1), 84-136.
Whipp, J. L. & Chiarelli, S. (2004). Self-regulation in a web-based course: a case study. Educational Technology Research and Development, 52(4), 5-22.
Yukselturk, E., & Bulut, S. (2007). Predictors for student success in an online course. Journal of Educational Technology & Society, 10(2), 71-83.