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VİDEO KAPILMA ÖLÇEĞİNİN UYARLAMA, GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI

Year 2019, Volume: 9 Issue: 1, 154 - 168, 31.01.2019
https://doi.org/10.17943/etku.439097

Abstract



Bu çalışmanın amacı; Visser ve diğerleri (2016) tarafından
geliştirilmiş olan Video Kapılma Ölçeğini

(VKÖ) Türkçe’ye uyarlamak ve uyarlanan ölçeğin geçerlik ve güvenirlik sınamalarını
yapmaktır. Özgün ölçek İngilizcedir, kuramsal olarak 5 faktörlü bir yapıdadır ve
toplam 15 maddeden oluşmaktadır. Ancak hemen belirtmek gerekir ki, özgün ölçeği
geliştiren yazarlar geçerlik sınamasında 4 faktörlü bir yapının daha iyi sonuç
verdiğini rapor etmiştir. Çalışmanın başında VKÖ’ni geliştiren yazarlardan
e-posta aracılığı ile ölçeği Türkçe’ye uyarlayabilmek için izin alınmıştır.
Daha sonra VKÖ Türkçe’ye çevrilmiş, dil ve içerik bakımından iyileştirme
yapıldıktan sonra Ankara’daki üç farklı üniversitenin BÖTE programlarında
öğrenim gören 253 öğrenciye uygulanmıştır. Uygulama sürecinde öğretmen
adaylarına öncelikle bilgisayar laboratuvarında gerçekleştirilen bir ilköğretim
“Bilişim Teknolojileri” dersinden kesit sunan 12 dakika uzunluğunda video-durum
izlettirilmiş, hemen sonrasında da ölçek uygulanmıştır. Gerçekleştirilen
doğrulayıcı faktör analizi, Türkçe VKÖ’nin özgün ölçekte kuramsal olarak sınanan
5 faktörlü yapıyı doğruladığını göstermiştir. Türkçe VKÖ’nin alt faktörleri ve
bütünü için Cronbach alfa iç-tutarlık katsayıları ise şöyledir: Faktör 1
(Dikkat) 0.57; Faktör 2 (Bir anlatı dünyasına girme) 0.73; Faktör 3 (Kimlik)
0.87; Faktör 4 (Empati) 0.78; Faktör 5 (Duygu) 0.69; ölçeğin bütünü 0.90. Sonuç
olarak, Türkçe’ye uyarlanan VKÖ, okullarda öğrencilere izlettirilen
video-durumların gerçekten izleyenleri ne derece içine çektiğini, dikkatlerini
bu öğretim materyaline ne derece verdiklerini ve izledikleri videodaki temel
karakterin yaşadıklarını ne düzeyde hissedip onunla ne kadar empati
kurduklarını belirlemede kullanılabilecek geçerli ve güvenilir bir ölçme
aracıdır. Üstelik bu ölçme aracı farklı farklı video-durumlar için
kullanılabilecek niteliktedir.



References

  • Blomberg, G., Sherin, M. G., Renkl, A., Glogger, I,. & Seidel, T. (2014). Understanding video as a tool for teacher education: Investigating instructional strategies to promote reflection. Instructional Science, 42(3), 443-463.
  • Colestock, A, & Sherin, M. G. (2009). Teachers’ sense-making strategies while watching video of mathematics instruction. Journal of Technology and Teacher Education, 17(1), 7-29.
  • De Leng, B. A., Dolmans, D.H., Van de Wiel, M.W., Muijtjens, A.M.M., & Van Der Vleuten, C.P. (2007). How video cases should be used as authentic stimuli in problem‐based medical education. Medical Education, 41(2), 181-188.
  • Dobrian, F., Sekar, V., Awan, A., Stoica, I., Joseph, D., Ganjam, A., ... & Zhang, H. (2011). Understanding the impact of video quality on user engagement. In ACM SIGCOMM Computer Communication Review, 41(4), 362-373.
  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 382-388.
  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th Ed.). Upper Saddle River: Pearson Prentice Hall.
  • Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press.
  • 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.
  • Kale, U. (2008) Levels of interaction and proximity: Content analysis of video-based classroom cases. The Internet and Higher Education, 11(2), 119-128.
  • Kim, S., Phillips, W. R., Pinsky, L., Brock, D., Phillips, K., & Keary, J. (2006). A conceptual framework for developing teaching cases: A review and synthesis of the literature across disciplines. Medical Education, 40(9), 867-876.
  • Kinzer, C. K. (1999). Educating for Democracy: Case-method Teaching and Learning, Victoria J. Risko Peabody College of Vanderbilt University Charles K. Kinzer Peabody College of Vanderbilt University. 47.
  • Klem, L. (2000). Structural equation modeling. In L. Grimm & P. Yarnold (Eds.), Reading and understanding multivariate statistics (Vol. II). Washington, DC: American Psychological Association.
  • Kline, R.B. (2005). Principles and practice of structural equation modeling. New York: Guilford.
  • Koeske, G. F. (1994). Some recommendations for improving measurement validation in social work research. Journal of Social Service Research, 18(3-4), 43-72.
  • LeFevre, D. M. (2004). Designing for teacher learning: Video-based curriculum design.In J Brophy (Ed.), Using video in teacher education (Vol. 10, pp. 235-258).
  • Lin, P. J. (2005). Using research-based video-cases to help pre-service primary teachers conceptualize a contemporary view of mathematics teaching. International Journal of Science and Mathematics Education, 3(3), 351-377.
  • McDonald, R. P., & Ho, M. H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64.
  • Merseth, K. K. (1996). Cases and case methods in teacher education. In J. Sikula, J. Buttery & E. Guyton (Eds.), Handbook of research on teacher education, (2nd Ed.). New York: Macmillan.
  • Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017.
  • Nunnally, J., & Berstein, I. H. C. (1994). Psycometric theory. New York: McGraw Hill.
  • Raykov, T., & Marcoulides, G. A. (2011). Introduction to psychometric theory. Routledge.
  • Santagata, R., Zannoni, C., & Stigler, J. W. (2007). The role of lesson analysis in pre-service teacher education: An empirical investigation of teacher learning from a virtual video-based field experience. Journal of Mathematics Teacher Education, 10(2), 123-140.
  • Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4-14.
  • Seidel, T., Blomberg, G., & Renkl, A. (2013). Instructional strategies for using video in teacher education. Teaching and Teacher Education, 34, 56-65.
  • Tabachnick, B. G., & Fidell, L. S. (2001). Computer-assisted research design and analysis (Vol. 748). Boston: Allyn and Bacon.
  • Teo, T. (2010). A path analysis of pre-service teachers' attitudes to computer use: Applying and extending the technology acceptance model in an educational context. Interactive Learning Environments, 18(1), 65-79.
  • Wang, J., & Hartley, K. (2003). Video technology as a support for teacher education reform. Journal of Technology and Teacher Education, 11(1), 105-138.
  • Wright, S. (1996). Case-based instruction: Linking theory to practice. Physical Educator, 53(4), 190.
  • Visser, L. N. C., Hillen, M. A., Verdam, M. G. E., Bol, N., de Haes, H. C. J. M., & Smets, E. M. A. (2016). Assessing engagement while viewing video vignettes: Validation of the Video Engagement Scale (VES). Patient Education and Counseling, 99(2), 227-235.
Year 2019, Volume: 9 Issue: 1, 154 - 168, 31.01.2019
https://doi.org/10.17943/etku.439097

Abstract

References

  • Blomberg, G., Sherin, M. G., Renkl, A., Glogger, I,. & Seidel, T. (2014). Understanding video as a tool for teacher education: Investigating instructional strategies to promote reflection. Instructional Science, 42(3), 443-463.
  • Colestock, A, & Sherin, M. G. (2009). Teachers’ sense-making strategies while watching video of mathematics instruction. Journal of Technology and Teacher Education, 17(1), 7-29.
  • De Leng, B. A., Dolmans, D.H., Van de Wiel, M.W., Muijtjens, A.M.M., & Van Der Vleuten, C.P. (2007). How video cases should be used as authentic stimuli in problem‐based medical education. Medical Education, 41(2), 181-188.
  • Dobrian, F., Sekar, V., Awan, A., Stoica, I., Joseph, D., Ganjam, A., ... & Zhang, H. (2011). Understanding the impact of video quality on user engagement. In ACM SIGCOMM Computer Communication Review, 41(4), 362-373.
  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 382-388.
  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th Ed.). Upper Saddle River: Pearson Prentice Hall.
  • Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press.
  • 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.
  • Kale, U. (2008) Levels of interaction and proximity: Content analysis of video-based classroom cases. The Internet and Higher Education, 11(2), 119-128.
  • Kim, S., Phillips, W. R., Pinsky, L., Brock, D., Phillips, K., & Keary, J. (2006). A conceptual framework for developing teaching cases: A review and synthesis of the literature across disciplines. Medical Education, 40(9), 867-876.
  • Kinzer, C. K. (1999). Educating for Democracy: Case-method Teaching and Learning, Victoria J. Risko Peabody College of Vanderbilt University Charles K. Kinzer Peabody College of Vanderbilt University. 47.
  • Klem, L. (2000). Structural equation modeling. In L. Grimm & P. Yarnold (Eds.), Reading and understanding multivariate statistics (Vol. II). Washington, DC: American Psychological Association.
  • Kline, R.B. (2005). Principles and practice of structural equation modeling. New York: Guilford.
  • Koeske, G. F. (1994). Some recommendations for improving measurement validation in social work research. Journal of Social Service Research, 18(3-4), 43-72.
  • LeFevre, D. M. (2004). Designing for teacher learning: Video-based curriculum design.In J Brophy (Ed.), Using video in teacher education (Vol. 10, pp. 235-258).
  • Lin, P. J. (2005). Using research-based video-cases to help pre-service primary teachers conceptualize a contemporary view of mathematics teaching. International Journal of Science and Mathematics Education, 3(3), 351-377.
  • McDonald, R. P., & Ho, M. H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64.
  • Merseth, K. K. (1996). Cases and case methods in teacher education. In J. Sikula, J. Buttery & E. Guyton (Eds.), Handbook of research on teacher education, (2nd Ed.). New York: Macmillan.
  • Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017.
  • Nunnally, J., & Berstein, I. H. C. (1994). Psycometric theory. New York: McGraw Hill.
  • Raykov, T., & Marcoulides, G. A. (2011). Introduction to psychometric theory. Routledge.
  • Santagata, R., Zannoni, C., & Stigler, J. W. (2007). The role of lesson analysis in pre-service teacher education: An empirical investigation of teacher learning from a virtual video-based field experience. Journal of Mathematics Teacher Education, 10(2), 123-140.
  • Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4-14.
  • Seidel, T., Blomberg, G., & Renkl, A. (2013). Instructional strategies for using video in teacher education. Teaching and Teacher Education, 34, 56-65.
  • Tabachnick, B. G., & Fidell, L. S. (2001). Computer-assisted research design and analysis (Vol. 748). Boston: Allyn and Bacon.
  • Teo, T. (2010). A path analysis of pre-service teachers' attitudes to computer use: Applying and extending the technology acceptance model in an educational context. Interactive Learning Environments, 18(1), 65-79.
  • Wang, J., & Hartley, K. (2003). Video technology as a support for teacher education reform. Journal of Technology and Teacher Education, 11(1), 105-138.
  • Wright, S. (1996). Case-based instruction: Linking theory to practice. Physical Educator, 53(4), 190.
  • Visser, L. N. C., Hillen, M. A., Verdam, M. G. E., Bol, N., de Haes, H. C. J. M., & Smets, E. M. A. (2016). Assessing engagement while viewing video vignettes: Validation of the Video Engagement Scale (VES). Patient Education and Counseling, 99(2), 227-235.
There are 30 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Deniz Deryakulu 0000-0002-6974-7183

Raziye Sancar 0000-0002-2875-9233

Ömer Faruk Ursavaş 0000-0002-5759-7894

Publication Date January 31, 2019
Published in Issue Year 2019 Volume: 9 Issue: 1

Cite

APA Deryakulu, D., Sancar, R., & Ursavaş, Ö. F. (2019). VİDEO KAPILMA ÖLÇEĞİNİN UYARLAMA, GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI. Eğitim Teknolojisi Kuram Ve Uygulama, 9(1), 154-168. https://doi.org/10.17943/etku.439097