Science Engagement Scale: Adaptation, Validation and Reliability Study

Yıl 2020, , 122 - 131, 31.12.2020

Gizem Turan Gürbüz , Esra Açıkgül Fırat , Murat Aydın

https://doi.org/10.17984/adyuebd.670173

Öz

In this study, The Science Engagement Scale developed by Wang et al. (2016) was adapted to Turkish and the validity / reliability studies were conducted. The original version of the scale consists of 4 dimensions and 33 items. These dimensions are; cognitive engagement, behavioral engagement, emotional engagement, and social engagement. During the adaptation phase, the items were translated into Turkish by three experts. The Turkish forms were examined and the draft form of the scale was obtained by the researchers. Then, the two experts of the two languages were examined through the language equivalence expert form for word usage and cultural suitability. The participants of the study consisted of 519 students in 6., 7., and 8. grades studied at two secondary schools in a small scale city in south east of Turkey during the 2019–2020 academic year. Convenience sampling method was used to determine the participants. Confirmatory factor analysis (CFA) was applied to the data obtained after the implementation. The fit index values obtained as a result of CFA (χ2 / df = 1.75; RMSEA = 0.038; SRMR = 0.049; RMR = 0.072; CFI = 0.98; NFI = 0.96) show that the 4-factor structure of the scale is acceptable. As a result of the reliability analyzes, the Cronbach alpha reliability coefficient of the Turkish form of the scale was 0.90 and the Guttman Split-half coefficient was 0.81. Finally, it can be said that the validity and reliability of the 33-item and 4-dimensional Turkish form of the scale adapted with this study can be used to determine student engagement in science classes.

Anahtar Kelimeler

Science education, Student engagement, Scale adaptation, Secondary school students

Kaynakça

• Arbuckle, J. L. (2005). AmosTM 6.0 user’s guide. Amos Development Corporation, USA.
• Aydın, G., Saka, M., & Guzey, S. (2017). Science, Technology, Engineering, Mathematic (STEM) Attitude Levels In Grades 4th - 8 th. Mersin University Journal of the Faculty of Education, 13(2).
• Baron, P., & Corbin, L. (2012). Student engagement: Rhetoric and reality. Higher Education Research & Development, 31(6), 759–772.
• Ceylan, Ö., Ermis, G. & Yıldız, G. (2018). Attitudes of Special Talented Students towards Science, Technology, Engineering, Mathematics (STEM) Education. International Conference on Gifted and Talented Education, İnönü University, Malatya.
• Czerniak, C. M., & Johnson, C. C. (2007). Interdisciplinary science teaching. Handbook of research on science education, 537-559.
• Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, S. (2012). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları (Vol. 2). Ankara: Pegem Akademi.
• Damar, A., Durmaz, C., & Önder, İ. (2017). Middle School Students’ Attitudes towards STEM Applications and Their Opinions about These Applications. Journal of Multidisciplinary Studies in Education, 1(1), 47-65.
• Eryılmaz, A. (2014). Üniversite öğrencileri için derse katılım ölçeklerinin geliştirilmesi. Usak Üniversitesi Sosyal Bilimler Dergisi, 7(2), 203-214.
• Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to Design and Evaluate Research in Education. New York: McGraw Hall.
• Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59-109.
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis 6th Edition. Pearson Prentice Hall. New Jersey.
• Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of management, 21(5), 967-988.
• Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
• 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.
• Hughes, J. N., Luo, W., Kwok, O., & Loyd, L. K. (2008). Teacher-student support, effortful engagement, and achievement: a three-year longitudinal study. Journal of Educational Psychology, 1, 1-14.
• Kahraman, N. (2014). Cross-grade comparison of relationship between students’ engagement and TIMSS 2011 science achievement. Education and Science (Large-ScaleAssessment Special Issue), 39 (172), 95-107.
• Kezar, A., & Elrod, S. (2012). Facilitating interdisciplinary learning: lessons from project kaleidoscope. Change: the magazine of higher learning, 44(1), 16-25.
• Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.
• Lesseig, K., Slavit, D., & Nelson, T. H. (2017). Jumping on the STEM bandwagon: How middle grades students and teachers can benefit from STEM experiences. Middle School Journal, 48(3), 15-24.
• MEB. (2018). İlköğretim Kurumları (İlkokullar ve Ortaokullar) Fen Bilimleri Dersi (3, 4, 5, 6, 7 ve 8. Sınıflar) Öğretim Programı, Ankara.
• Özdemir, A., Yaman, C., & Vural, R. A. (2018). STEM Uygulamaları Öğretmen Öz-yeterlik Ölçeğinin Geliştirilmesi: Bir Geçerlik ve Güvenirlik Çalışması. Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 5(2), 93-104.
• Reeve, J., & Tseng, C. M. (2011). Agency as a fourth aspect of students’ engagement during learning activities.Contemporary Educational Psychology, 36, 257–267. http://dx.doi.org/10.1016/j.cedpsych.2011.05.002
• Shaughnessy, J. M. (2013). Mathematics in a STEM context. Mathematics Teaching in the Middle school, 18(6), 324-324.
• 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.
• Suhr, D. (2006). Exploratory or confirmatory factor analysis. SAS Users Group International Conference (pp. 1 - 17). Cary: SAS Institute, Inc.
• Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics. London: Pearson.
• Turner, J. C., Christensen, A., Kackar-Cam, H. Z., Fulmer, S. M., & Trucano, M. (2018). The development of professional learning communities and their teacher leaders: An activity systems analysis. Journal of the Learning Sciences, 27(1), 49-88.
• Wang, M. T., & Degol, J. (2014). Staying engaged: Knowledge and research needs in student engagement. Child development perspectives, 8(3), 137-143.
• Wang, M. T., Fredricks, J. A., Ye, F., Hofkens, T. L., & Linn, J. S. (2016). The math and science engagement scales: Scale development, validation, and psychometric properties. Learning and Instruction, 43, 16-26.
• Wang, M. T., Willett, J. B., & Eccles, J. S. (2011). The assessment of school engagement: Examining dimensionality and measurement invariance across gender and race/ ethnicity. Journal of School Psychology, 49, 465-480.
• Willms, J. D., Friesen, S., & Milton, P. (2009). What did you do in school today? Transforming classrooms through social, academic and intellectual engagement. (First National Report) Toronto: Canadian Education Association.
• Yıldırım, İ., Başaran, M., Cücük, E., Yokuş, E. (2018). Development of Inquiry Based Teaching Self-Efficacy Scale for STEM+S Education: Validity and Reliability Study, International Online Journal of Educational Sciences, 10(3), 40-55.
• Yıldırım, G., Sökmen, Y., Yasemin, T. A. Ş., & Dilekmen, M. (2017). Öğrenci Katılım Ölçeğinin Türkçeye Uyarlanması: Geçerlik ve Güvenirlik Çalışması. Trakya Üniversitesi Eğitim Fakültesi Dergisi, 8(1), 68-79.
• You, H. S. (2017). Why Teach Science with an Interdisciplinary Approach: History, Trends, and Conceptual Frameworks. Journal of Education and Learning, 6(4), 66-77.