Esnek Öğrenme Ortamı İlgi Ölçeğinin Geliştirilmesi

Yıl 2024, Cilt: 15 Sayı: 2, 1817 - 1840, 28.08.2024

Seval Fer , Esma Genç , İlker Cırık , İbrahim Uysal , Levent Ertuna , Sevilay Yıldız , Murat Debbağ , Melih Derya Gürer , Hülya Pehlivan , Derya Karadeniz , Yasemin Kuzgun , Fatih Karataş

https://doi.org/10.51460/baebd.1506845

Öz

Son yıllarda etkili öğrenme üzerindeki artan vurgu ve bilişim teknolojilerindeki hızlı değişimler, öğretim kurumlarında tüm öğrencilerin ihtiyaçlarını karşılayan esnek bir öğrenme anlayışının öneminin altını çizmektedir. Bu çalışmada, belirtilen önemden yola çıkarak öğretmen adaylarının esnek öğrenme ortamlarına ilgisini belirlemek için bir ölçme aracı geliştirmek amaçlanmaktadır. Çalışmada ilgili alan yazın doğrultusunda 36 adet aday madde oluşturulmuş ve "konu alanı (5), ölçme ve değerlendirme (5) uzmanlarının görüşleri" sonrasında kalan 30 madde ile deneme uygulaması gerçekleştirilmiştir. Beşli Likert tipi ölçeğin geliştirilmesi için iki ayrı örneklem üzerinde çalışılmıştır. Araştırmanın birinci örneklemi üç farklı üniversitede öğrenim görmekte olan 469 öğretmen adayından, ikinci örneklemi ise iki farklı üniversitede öğrenim görmekte olan 329 öğretmen adayından oluşmaktadır. Birinci örneklemden elde edilen veriler ile açımlayıcı faktör analizi kullanılarak ölçeğin mevcut faktör yapısı ortaya konmuş; madde analizleri ve güvenirlik incelemesi gerçekleştirilmiştir. İkinci örneklemden elde edilen veriler ile deneme uygulaması sonucunda elde edilen yapı doğrulayıcı faktör analiziyle incelenmiştir. Ayrıca ikinci örneklem üzerinde tekrar güvenirlik alınmıştır. Açımlayıcı faktör analizi sonucunda açıklanan varyans oranı %44 olan 29 madde ve tek boyuttan oluşan bir yapıya erişilmiştir. Düzeltilmiş madde-toplam korelasyon değerleri, ölçek maddelerinin yeterince ayırt edici olduğunu göstermiştir. Doğrulayıcı faktör analizi sonucunda elde edilen uyum indeksleri ilgili kriterleri karşıladığından model uyumunun sağlandığı belirtilebilir. Çalışmada güvenirliği belirlemek için incelenen Cronbach alfa ve McDonald omega katsayıları, her iki örneklemde de .92 olarak hesaplanmıştır. Elde edilen katsayılar ölçekten elde edilen puanların güvenilir olduğunu göstermektedir.

Anahtar Kelimeler

esnek öğrenme, ölçek geliştirme, öğretmen adayı

Etik Beyan

Bu çalışma kapsamında 21.05.2022 tarihinde Hacettepe Üniversitesi Etik Kurulu’ndan E-35853172-600-00002203304 sayı numarası ile, 27.05.2022 tarihinde Bolu Abant İzzet Baysal Üniversitesi Etik Kurulundan 2022/233 sayı numarası ile etik kurul izni alınmıştır. İlgili izinler sisteme yüklenmiştir.

Destekleyen Kurum

Tübitak

Proje Numarası

122G041

Teşekkür

Bu çalışma, TÜBİTAK 3005 - Sosyal ve Beşeri Bilimlerde Yenilikçi Çözümler Araştırma Projeleri Destekleme Programı tarafından desteklenen “Yükseköğretimde Dönüştürülmüş Öğrenme Ortamları için Esnek Öğretim Tasarımı Modeli Geliştirme Çalışması” başlıklı 122G041 nolu projeden üretilmiştir.

Development of Flexible Learning Environment Interest Scale

Öz

In recent years, the increasing emphasis on effective learning and the rapid changes in information technology have underscored the importance of a flexible learning approach that meets the needs of all students in educational institutions. This study aims to develop a measurement tool to determine the interest of pre-service teachers in flexible learning environments, building on the highlighted importance. In line with the relevant literature, 36 candidate items were created, and after obtaining expert opinions from "subject area (5) and assessment and evaluation (5) experts," a trial application was conducted with the remaining 30 items. The development of the five-point Likert scale involved two separate samples. The first sample consisted of 469 pre-service teachers from three different universities, while the second sample included 329 pre-service teachers from two different universities. Exploratory factor analysis (EFA) was conducted using data from the first sample to identify the existing factor structure of the scale, followed by item analyses and reliability examination. Confirmatory factor analysis (CFA) was performed on the data from the second sample to validate the structure obtained from the trial application. Additionally, reliability was re-evaluated on the second sample. The EFA revealed a structure comprising 29 items and a single dimension, explaining 44% of the variance. Corrected item-total correlation values indicated that the scale items were sufficiently discriminatory. The CFA results showed that the fit indices met the relevant criteria, indicating model fit. Cronbach's alpha and McDonald's omega coefficients, examined for reliability, were both calculated as .92 for both samples. These coefficients demonstrate that the scores obtained from the scale are reliable.

Anahtar Kelimeler

flexible learning, scale development, pre-service teacher

Proje Numarası

122G041

Kaynakça

• Altman, D. G. (1991). Practical statistics for medical research. CRC.
• Ary, D., Jacobs, L. C., Sorensen, C., & Walker, D. A. (2018). Introduction to research in education (10th ed.). Cengage Learning.
• Beavers, A. S., Lounsbury, J. W., Richards, J. K., Huck, S. W., Skolits, G. J., & Esquivel, L. (2013). Practical considerations for using exploratory factor analysis in educational research. Practical Assessment, Research and Evaluation, 18(6), 1-13. https://doi.org/10.7275/qv2q-rk76
• Bergamin, P. B., Ziska, S., Werlen, E., & Siegenthaler, E. (2012). The relationship between flexible and self-regulated learning in open and distance universities. International Review of Research in Open and Distributed Learning, 13(2), 101-123.
• Bergamin, P., Ziska, S., & Groner, R. (2009). Structural equation modeling of factors affecting success in student’s performance in ODL-programs: Extending quality management concepts. Open Praxis, 4(1), 18-25.
• Bridgland, A., & Blanchard, P. (2001). Flexible delivery/flexible learning…does it make a difference?. Australian Academic & Research Libraries, 32(3), 177-191. https://doi.org/10.1080/00048623.2001.10755158
• Bowles , M. S. (2004). Relearning to e‐learn. Melbourne University Press.
• Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005
• Creswell, J. W. (2019). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (6th ed.). Pearson Education, Inc.
• Collis, B., Vingerhoets, J., & Moonen, J. (1997). Flexibility as a key construct in European training: Experiences from the TeleScopia Project. British Journal of Educational Technology, 28(3), 199–217. https://doi.org/10.1111/1467-8535.00026
• Deakin University Report (2009). Introducing flexible learning. Retrieved from http://www.deakin.edu.au
• Diezmann, C. M., & Yelland, N. J. (2000). Being flexible about flexible learning and flexible delivery. In L. Richardson & J. Lidstone (Eds.), Proceedings ASET-HERDSA 2000 Conference, Toowoomba.
• Dikilitas, K. (2023). Conceptual framework for flexible learning design: The context of flipped classroom. https://kudos.dfo.no/documents/77155/files/37244.pdf
• Erkuş, A. (2014). Psikolojide ölçme ve ölçek geliştirme-I (2. baskı). Pegem Akademi.
• Evans, T. (2000). Flexible delivery and flexible learning: Developing flexible learners? In V. Jakupec & J. Garrick (Eds.), Flexible learning and HRD: Putting theory to work (pp. 211–224). Routledge.
• Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272
• Ferrando, P. J., & Lorenzo-Seva, U. (2017). Program FACTOR at 10: Origins, development and future directions. Psicothema, 29(2), 236–240. https://doi.org/10.7334/psicothema2016.304
• Finney, S. J., & DiStefano, C. (2013). Nonnormal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 439-492). IAP.
• Forero, C. G., Maydeu-Olivares, A., & Gallardo-Pujol, D. (2009). Factor analysis with ordinal indicators: A Monte Carlo study comparing DWLS and ULS estimation. Structural Equation Modeling, 16(4), 625-641. https://doi.org/10.1080/10705510903203573
• Graham, M., Milanowski, A., & Miller, J. (2012). Measuring and promoting inter-rater agreement of teacher and principal performance ratings. Report of the Center for Educator Compensation Reform. Retrieved from https://files.eric.ed.gov/fulltext/ED532068.pdf
• Gwet, K. L. (2014). Handbook of inter-rater reliability: The definitive guide to measuring the extent of agreement among raters. Advanced Analytics, LLC.
• Gwet, K. L. (2019). irrCAC: Computing chance-corrected agreement coefficients (CAC) (Version 1.0) [Computer software]. https://CRAN.R-project.org/package=irrCAC
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (9th ed.). Prentice-Hall.
• Harper, B., Oliver, R. G., & Agostinho, S. (2001). Developing generic tools for use in flexible learning: A preliminary progress report. Proceedings of 18th Conference of the Australasian Society for Computers in Learning in Tertiary Education, (pp. 253-62). Retrieved from https://www.academia.edu/download/48847768/download.pdf
• Hart, I. (2000). Learning and the ‘F’ word. Educational Media International, 37(2), 98-101. https://doi.org/10.1080/095239800410388
• Hermano, J. R., & Denamarca, S. (2022). Perceived Learning Difficulties of Students in Flexible Learning in a Philippine State College. International Journal of Educational Research Review, 7(4), 244-252.
• Hill, J. R. (2006). Flexible learning environments: Leveraging the affordances of flexible delivery and flexible learning. Innovative Higher Education, 31, 187-197.
• 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. https://doi.org/10.1080/10705519909540118
• JASP Team. (2023). JASP (Version 0.17.1) [Computer software]. https://jasp-stats.org/
• Joan, D. R. (2013). Flexible learning as new learning design in classroom process to promote quality education. Journal on School Educational Technology, 9(1), 37-42.
• Joaquin, J. J. B., Biana, H. T., & Dacela, M. A. (2020, October). The Philippine higher education sector in the time of COVID-19. In Frontiers in Education (Vol. 5, p. 208). Frontiers. https://doi.org/10.3389/feduc.2020.576371.
• Johnson, B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). SAGE Publications.
• Kline, R. B. (2016). Principle and practice of structural equation modeling (4th ed.). The Guilford.
• Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310
• Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
• Lee, C. T., Zhang, G., & Edwards, M. C. (2012). Ordinary least squares estimation of parameters in exploratory factor analysis with ordinal data. Multivariate Behavioral Research, 47, 314–339. https://doi.org/10.1080/00273171.2012.658340
• LI, K. C. (2014). How flexible do students prefer their learning to be?. Asian Association of Open Universities Journal, 9(1), 35-46.
• Li, K. C., & Wong, B. Y. Y. (2018). Revisiting the definitions and implementation of flexible learning. In K. C. Li, K. S. Yuen, & B. T. M. Wong (Eds.), Innovations in open and flexible education (pp. 3–13). Springer. https://doi.org/10.1007/978-981-10-7995-5_1
• Li, Y. L. (2014). Confirmatory factor analysis with continuous and ordinal data: An empirical study of stress level [Master’s thesis]. Uppsala University.
• Lim, D. H. (2004). The effect of flexible learning schedule on online learners' learning, application, and instructional perception. Online Submission.
• Loon, M. (2017). Designing and developing digital and blended learning solutions. Chartered Institute of Personnel and Development.
• Lorenzo-Seva, U., & Ferrando, P. J. (2022). Factor (Version 12.02.01) [Computer software]. Tarragona: Universitat Rovira i Virgili.
• Lundin, R. (1999). Flexible teaching and learning: Perspectives and practices. In Proceedings of the Australian Conference on Science and Mathematics Education.
• Mardia, K. V. (1970). Measures of multivariate skewnees and kurtosis with applications. Biometrika, 57(3), 519-530. https://doi.org/10.2307/2334770
• Müller, C., Mildenberger, T., & Steingruber, D. (2023). Learning effectiveness of a flexible learning study programme in a blended learning design: Why are some courses more effective than others?. Int J Educ Technol High Educ, 20(10). https://doi.org/10.1186/s41239-022-00379-x
• Nunan, T. (2000) Exploring the concept of flexibility. In V. Jakupec & J. Garrick (Eds.), Flexible learning and HRD: Putting theory to work (pp. 211–224). Routledge.
• Nunnally, J., & Bernstein, I. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Price, L. R. (2017). Psychometric methods: Theory into practice. Guilford
• RStudio Team (2021). RStudio: Integrated development environment for R [Computer software]. Retrieved from http://www.rstudio.com
• Soffer, T., Kahan, T., & Nachmias, R. (2019). Patterns of students’ utilization of flexibility in online academic courses and their relation to course achievement. International Review of Research in Open and Distributed Learning, 20(3).
• Stevens, J. (2009). Applied multivariate statistics for the social sciences (5th edition). New York: Taylor & Francis. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
• The Jamovi Project (2023). Jamovi (Version 2.4.8) [Computer Software]. https://www.jamovi.org
• Thorndike, R. M., & Thorndike-Christ, T. (2014). Measurement and evaluation in psychology and education. Pearson.
• Timmerman, M. E., & Lorenzo-Seva, U. (2011). Dimensionality assessment of ordered polytomous items with parallel analysis. Psychological Methods, 16(2), 209-220. https://doi.org/10.1037/a0023353
• Veletsianos, G., & Houlden, S. (2019). An analysis of flexible learning and flexibility over the last 40 years of distance education. Distance Education, 40(4), 454-468. https://doi.org/10.1080/01587919.2019.1681893
• Wheaton, B., Muthén, B., Alwin, D., & Summers, G. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84-136. https://doi.org/10.2307/270754
• Yang, Y., & Liang, X. (2013). Confirmatory factor analysis under violations of distributional and structural assumptions. International Journal of Quantitative Research in Education, 1(1), 61-84. https://doi.org/10.1504/ijqre.2013.055642