Research Article
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Matematik ve Fen Derslerine Katılım Ölçeğinin Fizik Dersi Bağlamında Türkçeye Uyarlanması

Year 2020, Volume: 7 Issue: 2, 1 - 15, 25.05.2020
https://doi.org/10.33907/turkjes.661339

Abstract

Bu çalışmada, Türk öğrencilerinin fizik dersine katılım düzeyini belirlemek için kullanılacak olan Matematik ve Fen Derslerine Katılım Ölçeği Türkçeye uyarlanmıştır. Orijinali İngilizce olan ölçek, davranışsal, duygusal, bilişsel ve sosyal katılım olmak üzere 4 boyuttan ve 33 maddeden oluşmaktadır. Ölçek, Türkçeye geri çeviri yöntemi kullanılarak ve belirli basamaklar takip edilerek uyarlanmıştır. Türkçeye çevrilen bu form, İstanbul ilindeki bir anadolu lisesinde okuyan 398 öğrenciye (215 Kız, 181 Erkek) uygulanmıştır. Ölçek, fizik dersine katılımı ölçtüğü için çalışmaya katılan öğrencilerin aktif olarak fizik dersi alıp almadığına dikkat edilmiştir. Bu bağlamda çalışmaya 127 dokuzuncu sınıf öğrencisi, 117 onuncu sınıf öğrencisi ve sayısal alanda okuyan 154 on birinci sınıf öğrencisi katılmıştır. Çalışma verileri üzerinden Türkçe formun orijinal ölçeğin öngördüğü dörtlü faktör yapısına sahip olup olmadığı, Doğrulayıcı Faktör Analizi (DFA) yapılarak test edilmiştir. Yapılan DFA, Türkçe formun orijinal ölçekteki dörtlü faktör yapısına sahip olduğunu göstererek, ölçeğin yapı geçerliği hakkında kanıt sunmuştur. Cronbach alfa güvenirlik değeri tüm ölçek ve alt boyutlar için istenilen değerlerde olmuştur. Sonuç olarak, bulgular uyarlanmış Türkçe formun geçerli ve güvenilir bir ölçüm aracı olduğunu desteklemektedir.

Supporting Institution

Boğaziçi Üniversitesi, Bilimsel Araştırma Projeleri

Project Number

19D03SUP1

References

  • Appleton, J. J., Christenson, S. L., ve Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45(5), 369-386.
  • Archambault, I., Janosz, M., Fallu, J. S., ve Pagani, L. S. (2009). Student engagement and its relationship with early high school dropout. Journal of adolescence, 32(3), 651-670.
  • Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50(1), 84-94.
  • Cohen, J., ve Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillside, NJ: Prentice Hall.
  • DeWitt, J., Archer, L., Osborne, J., Dillon, J., Willis, B., ve Wong, B. (2011). High aspirations but low progression: the science aspirations–careers paradox amongst minority ethnic students. International Journal of Science and Mathematics Education, 9(2), 243-271.
  • Eccles, J. S., veWang, M. (2012). Part I commentary: Part I commentary: So what is student engagement anyway. In S. L. Christenson, A. L. Reschly, ve C.Wylie (Eds.), Handbook of research on student engagement (pp. 133–145).NewYork: Springer Sciences.
  • Everson , H.T, Tobias, S (1998). The ability to estimate knowledge and performance in college: A metacognitive analysis. Instructional Science, 26: 65-79.
  • Fraenkel, J. R., Wallen, N. E. ve Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). New York: McGraw-Hill, Inc.
  • Fredricks, J. A., Blumenfeld, P. C., ve Paris, A. H. (2004). School engagement: Potential of the concept,state of the evidence. Review of Educational Research, 74, 59–109.
  • Fredricks, J. A., ve McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In Handbook of research on student engagement (pp. 763-782). Springer, Boston, MA.
  • Fredricks, J. A., Wang, M. T., Linn, J. S., Hofkens, T. L., Sung, H., Parr, A., ve Allerton, J. (2016). Using qualitative methods to develop a survey measure of math and science engagement. Learning and Instruction, 43, 5-15.
  • Green, J., Liem, G. A. D., Martin, A. J., Colmar, S., Marsh, H. W., ve McInerney, D. (2012). Academic motivation, self-concept, engagement, and performance in high school: Key processes from a longitudinal perspective. Journal of adolescence, 35(5), 1111-1122.
  • Hazel, E., Prosser, M., ve Trigwell, K. (2002). Variation in learning orchestration in university biology courses. International Journal of Science Education, 24(7), 737-751.
  • Hughes, J. N., Luo, W., Kwok, O. M., ve Loyd, L. K. (2008). Teacher-student support, effortful engagement, and achievement: A 3-year longitudinal study. Journal of Educational Psychology, 100(1), 1-14.
  • Jöroskog, K. G., ve Sörbom, D. (1993). LISREL 8:Structural equation modeling with SIMPLIS command language. Chicago: Scientific Software International.
  • Kortering, L. J., ve Christenson, S. (2009). Engaging students in school and learning: The real deal for school completion. Exceptionality, 17(1), 5-15.
  • Maltese, A. V., ve Tai, R. H. (2010). Eyeballs in the fridge: Sources of early interest in science. International Journal of Science Education, 32(5), 669-685.
  • Martin, A. J., Way, J., Bobis, J., ve Anderson, J. (2015). Exploring the ups and downs of mathematics engagement in the middle years of school. The Journal of Early Adolescence, 35(2), 199-244.
  • Pallant, J. (2001). SPSS Survival Manual: A step by step guide to data analysis using SPSS for Windows (Versions 10 and 11). Maidenhead, Philadelphia: Open University Press.
  • Schermelleh-Engel, K., Moosbrugger, H., ve Muller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research, 8(2), 23–74.
  • Sinatra, G. M., Heddy, B. C., ve Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50 (1), 1-13.
  • Skinner, E. A., Kindermann, T. A., Connell, J. P., ve Wellborn, J. G. (2009). Engagement and disaffection as organizational constructs in the dynamics of motivational development. In K. Wentzel ve A. Wigfield (Eds.), Handbook of motivation in school (pp. 223–245). Mahwah, NJ: Erlbaum.
  • Skinner, E. A., & Pitzer, J. R. (2012). Developmental dynamics of student engagement, coping, and everyday resilience. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 21–44). New York, NY: Springer.
  • Stevens, J. (2002). Applied multivariate statistics for the social sciences. Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc., Publishers.
  • Su, Y. L., ve Reeve, J. (2011). A meta-analysis of the effectiveness of intervention programs designed to support autonomy. Educational Psychology Review, 23(1), 159-188.
  • Tytler, R., ve Osborne, J. (2012). Student attitudes and aspirations towards science. In B. J. Fraser, K. Tobin, ve C. J. McRobbie (Eds.), Second international handbook of science education (pp. 597–625). New York, NY: Springer International.
  • Wang, M. T. (2012). Educational and career interests in math: A longitudinal examination of the links between classroom environment, motivational beliefs, and interests. Developmental Psychology, 48(6), 1643- 1657.
  • Wang, M. T., ve Degol, J. (2014). Staying engaged: Knowledge and research needs in student engagement. Child development perspectives, 8(3), 137-143.
  • Wang, M. T., ve Holcombe, R. (2010). Adolescents’ perceptions of school environment, engagement, and academic achievement in middle school. American Educational Research Journal, 47(3), 633-662.
  • Wang, M. T., Fredricks, J. A., Ye, F., Hofkens, T. L., ve Linn, J. S. (2016). The Math and Science Engagement Scales: Scale development, validation, and psychometric properties. Learning and Instruction, 43, 16-26.
  • Yerdelen-Damar, S., ve Peşman, H. (2013). Relations of gender and socioeconomic status to physics through metacognition and self-efficacy. The Journal of Educational Research, 106(4), 280-289.
Year 2020, Volume: 7 Issue: 2, 1 - 15, 25.05.2020
https://doi.org/10.33907/turkjes.661339

Abstract

Project Number

19D03SUP1

References

  • Appleton, J. J., Christenson, S. L., ve Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45(5), 369-386.
  • Archambault, I., Janosz, M., Fallu, J. S., ve Pagani, L. S. (2009). Student engagement and its relationship with early high school dropout. Journal of adolescence, 32(3), 651-670.
  • Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50(1), 84-94.
  • Cohen, J., ve Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillside, NJ: Prentice Hall.
  • DeWitt, J., Archer, L., Osborne, J., Dillon, J., Willis, B., ve Wong, B. (2011). High aspirations but low progression: the science aspirations–careers paradox amongst minority ethnic students. International Journal of Science and Mathematics Education, 9(2), 243-271.
  • Eccles, J. S., veWang, M. (2012). Part I commentary: Part I commentary: So what is student engagement anyway. In S. L. Christenson, A. L. Reschly, ve C.Wylie (Eds.), Handbook of research on student engagement (pp. 133–145).NewYork: Springer Sciences.
  • Everson , H.T, Tobias, S (1998). The ability to estimate knowledge and performance in college: A metacognitive analysis. Instructional Science, 26: 65-79.
  • Fraenkel, J. R., Wallen, N. E. ve Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). New York: McGraw-Hill, Inc.
  • Fredricks, J. A., Blumenfeld, P. C., ve Paris, A. H. (2004). School engagement: Potential of the concept,state of the evidence. Review of Educational Research, 74, 59–109.
  • Fredricks, J. A., ve McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In Handbook of research on student engagement (pp. 763-782). Springer, Boston, MA.
  • Fredricks, J. A., Wang, M. T., Linn, J. S., Hofkens, T. L., Sung, H., Parr, A., ve Allerton, J. (2016). Using qualitative methods to develop a survey measure of math and science engagement. Learning and Instruction, 43, 5-15.
  • Green, J., Liem, G. A. D., Martin, A. J., Colmar, S., Marsh, H. W., ve McInerney, D. (2012). Academic motivation, self-concept, engagement, and performance in high school: Key processes from a longitudinal perspective. Journal of adolescence, 35(5), 1111-1122.
  • Hazel, E., Prosser, M., ve Trigwell, K. (2002). Variation in learning orchestration in university biology courses. International Journal of Science Education, 24(7), 737-751.
  • Hughes, J. N., Luo, W., Kwok, O. M., ve Loyd, L. K. (2008). Teacher-student support, effortful engagement, and achievement: A 3-year longitudinal study. Journal of Educational Psychology, 100(1), 1-14.
  • Jöroskog, K. G., ve Sörbom, D. (1993). LISREL 8:Structural equation modeling with SIMPLIS command language. Chicago: Scientific Software International.
  • Kortering, L. J., ve Christenson, S. (2009). Engaging students in school and learning: The real deal for school completion. Exceptionality, 17(1), 5-15.
  • Maltese, A. V., ve Tai, R. H. (2010). Eyeballs in the fridge: Sources of early interest in science. International Journal of Science Education, 32(5), 669-685.
  • Martin, A. J., Way, J., Bobis, J., ve Anderson, J. (2015). Exploring the ups and downs of mathematics engagement in the middle years of school. The Journal of Early Adolescence, 35(2), 199-244.
  • Pallant, J. (2001). SPSS Survival Manual: A step by step guide to data analysis using SPSS for Windows (Versions 10 and 11). Maidenhead, Philadelphia: Open University Press.
  • Schermelleh-Engel, K., Moosbrugger, H., ve Muller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research, 8(2), 23–74.
  • Sinatra, G. M., Heddy, B. C., ve Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50 (1), 1-13.
  • Skinner, E. A., Kindermann, T. A., Connell, J. P., ve Wellborn, J. G. (2009). Engagement and disaffection as organizational constructs in the dynamics of motivational development. In K. Wentzel ve A. Wigfield (Eds.), Handbook of motivation in school (pp. 223–245). Mahwah, NJ: Erlbaum.
  • Skinner, E. A., & Pitzer, J. R. (2012). Developmental dynamics of student engagement, coping, and everyday resilience. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 21–44). New York, NY: Springer.
  • Stevens, J. (2002). Applied multivariate statistics for the social sciences. Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc., Publishers.
  • Su, Y. L., ve Reeve, J. (2011). A meta-analysis of the effectiveness of intervention programs designed to support autonomy. Educational Psychology Review, 23(1), 159-188.
  • Tytler, R., ve Osborne, J. (2012). Student attitudes and aspirations towards science. In B. J. Fraser, K. Tobin, ve C. J. McRobbie (Eds.), Second international handbook of science education (pp. 597–625). New York, NY: Springer International.
  • Wang, M. T. (2012). Educational and career interests in math: A longitudinal examination of the links between classroom environment, motivational beliefs, and interests. Developmental Psychology, 48(6), 1643- 1657.
  • Wang, M. T., ve Degol, J. (2014). Staying engaged: Knowledge and research needs in student engagement. Child development perspectives, 8(3), 137-143.
  • Wang, M. T., ve Holcombe, R. (2010). Adolescents’ perceptions of school environment, engagement, and academic achievement in middle school. American Educational Research Journal, 47(3), 633-662.
  • Wang, M. T., Fredricks, J. A., Ye, F., Hofkens, T. L., ve Linn, J. S. (2016). The Math and Science Engagement Scales: Scale development, validation, and psychometric properties. Learning and Instruction, 43, 16-26.
  • Yerdelen-Damar, S., ve Peşman, H. (2013). Relations of gender and socioeconomic status to physics through metacognition and self-efficacy. The Journal of Educational Research, 106(4), 280-289.
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Other Fields of Education
Journal Section Research Article
Authors

Sevda Yerdelen-damar

Fikret Korur

Havva Sağlam

Project Number 19D03SUP1
Publication Date May 25, 2020
Submission Date December 18, 2019
Acceptance Date March 29, 2020
Published in Issue Year 2020 Volume: 7 Issue: 2

Cite

APA Yerdelen-damar, S., Korur, F., & Sağlam, H. (2020). Matematik ve Fen Derslerine Katılım Ölçeğinin Fizik Dersi Bağlamında Türkçeye Uyarlanması. Turkish Journal of Educational Studies, 7(2), 1-15. https://doi.org/10.33907/turkjes.661339