Türkiye'nin PISA 2015 Fen Başarısının ve İlişkili Değişkenlerin Hiyerarşik Doğrusal Modelleme İle İncelenmesi

Yıl 2020, Cilt: 14 Sayı: 1, 450 - 480, 30.06.2020

Mustafa Yıldız Eda Erdaş Kartal Günkut Mesci

https://doi.org/10.17522/balikesirnef.663737

Öz

Bu çalışmanın amacı, PISA 2015'in Türkiye'deki lise öğrencilerinin fen başarısına ilişkin değişkenleri hiyerarşik doğrusal modelleme (HLM) yaklaşımı kullanarak araştırmaktır. Bulgular, öğrencilerin fen başarısı ile demografik özellikleri arasındaki ilişkinin, fen başarısının duyuşsal alan ve öğrenme ortamı ile arasındaki ilişkiden daha güçlü olduğunu ortaya koymuştur. Bu bulgulara dayanarak, öğrencilerin fen başarısını geliştirmenin, başarıyı etkileyen değişkenleri dikkate alarak, özellikle de demografik değişkenler ve kapsayıcı okul sistemlerinin inşası bağlamında eşit fırsatlar sağlayabilen gelişmelerle mümkün olabileceği düşünülmektedir.

Anahtar Kelimeler

fen başarısı, PİSA, HLM

Investigation of Turkey's PISA 2015 Science Achievement and Associated Variables Using Hierarchical Linear Modeling

Öz

The purpose of this study is to investigate the variables related to science achievement of high school students in Turkey portion of PISA 2015, by using hierarchical linear modeling (HLM) approach. The findings revealed that the relationship between the science achievement and demographic characteristics of the students is stronger than the relationship between science achievement and the affective domain as well as the learning environment. Based on these findings, it is thought that improving students’ science achievement is possible by taking into account the variables that affect the success, especially with improvements that can provide equal opportunities in the context of demographic variables and the construction of inclusive school systems.

Anahtar Kelimeler

science achivement, pisa, hlm

Kaynakça

• Acar, T., & Ogretmen, T. (2012). Analysis of 2006 PISA science performance via multilevel statistical methods. Education and Science. 37(163), 178-189.
• Albright, J. J., & Marinova, D. M. (2010). Estimating multilevel models using SPSS, Stata, SAS, and R. Bloomington, IN: Indiana University.
• Akaike, H. (1987). Factor analysis and AIC. In Selected Papers of Hirotugu Akaike (pp. 371-386). Springer, New York, NY.
• Akkuş, N. (2008). Yaşam boyu öğrenme becerilerinin göstergesi olarak 2006 PISA sonuçlarının Türkiye açısından değerlendirilmesi [Life-long learning skills as an indicator of the PISA 2006 results in terms of assessing Turkey]. Master thesis, Hacettepe University, Ankara, Turkey.
• Alivernini, F., & Manganelli, S. (2015). Country, school and students factors associated with extreme levels of science literacy across 25 countries. International Journal of Science Education, 37(12), 1992-2012.
• Areepattamannil, S. (2014). International Note: What factors are associated with reading, mathematics, and science literacy of Indian adolescents? A multilevel examination. Journal of adolescence, 37(4), 367-372.
• Areepattamannil, S., Chiam, C. L., Lee, D. H., & Hong, H. (2015). Correlates of science achievement in Singapore: a multilevel exploration. In Science Education in East Asia (pp. 607-629). Springer, Cham.
• Aydoğdu, B. (2006). İlköğretim fen ve teknoloji dersinde bilimsel süreç becerilerini etkileyen değişkenlerin belirlenmesi [Identification of variables effecting science process skills in primary science and technology course]. Doctoral dissertation. Dokuz Eylül University, Izmir, Turkey.
• Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1-48.
• Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345-370.
• Buuren, S. V., & Groothuis-Oudshoorn, K. (2010). Mice: Multivariate imputation by chained equations in R. Journal of statistical software, 55(2), 1-68.
• Bybee, R. & McCrae, B. (2011). Scientific literacy and student attitudes: Perspectives from PISA 2006 science. International Journal of Science Education, 33(1), 7-26.
• Cairns, D., & Areepattamannil, S. (2017). Exploring the relations of inquiry-based teaching to science achievement and dispositions in 54 countries. Research in Science Education, 49(1), 1-23.
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. New York, NY: Psychology Press.
• De Ayala, R. J. (2013). The theory and practice of item response theory. Guilford Publications.
• Enders, C. K. (2010). Applied missing data analysis. Guilford press.
• Finch, W. H., Bolin, J. E., & Kelley, K. (2016). Multilevel modeling using R. Boca Raton: Crc Press.
• Grund, S., Robitzsch, A., & Luedtke, O. (2018). Mitml: Tools for Multiple Imputation in Multilevel Modeling (2018). R package version 0.3-6.
• Gürsakal, S. (2012). PISA 2009 öğrenci başari düzeylerini etkileyen faktörlerin değerlendirilmesi [An evaluation of PISA 2009 student achievement levels’ affecting factors]. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(1), 441-452.
• Güzel, Ç. I., & Berberoğlu, G. (2005). An analysis of the Programme for International Student Assessment 2000 (PISA 2000) mathematical literacy data for Brazilian, Japanese, and Norwegian students. Studies in Educational Evaluation, 31, 283-314.
• Jiang, F., & McComas, W. F. (2015). The effects of inquiry teaching on student science achievement and attitudes: Evidence from propensity score analysis of PISA data. International Journal of Science Education, 37(3), 554-576.
• Kıncal, R. Y., & Yazgan, A. D. (2010). İlköğretim 7. ve 8. sınıf öğrencilerinin formal operasyonel düşünme becerilerinin bazı değişkenler açısından incelenmesi [Investigating the Formal Operational Thinking Skills of 7th and 8th Grade Primary School Students According to Some Variables]. Elementary Education Online, 9(2), 723-733

• Kocabaş, İ., Aladağ, S., & Yavuzalp, N. (2004). Eğitim sistemimizdeki okullaşma oranlarının analizi [Analysis of schooling rates in our education system]. Paper presented at XIII. National Educational Sciences Congress, Inonu University, Malatya.
• Lam, T. Y. P., & Lau, K. C. (2014). Examining factors affecting science achievement of Hong Kong in PISA 2006 using hierarchical linear modeling. International Journal of Science Education, 36(15), 2463-2480.
• Mason, L., Boscolo, P., Tornatora, M. C., & Ronconi, L. (2013). Besides knowledge: A cross-sectional study on the relations between epistemic beliefs, achievement goals, self-beliefs, and achievement in science. Instructional Science, 41(1), 49-79.
• Ministry of National Education (2003). PISA 2003 ulusal rapor [PISA 2003 National Report]. Retivered from PISA Turkey web site: http://pisa.meb.gov.tr/?page_id=22 Milli Eğitim Bakanlığı Talim Terbiye Kurulu Başkanlığı, MEB (2005). İlköğretim fen ve teknoloji dersi öğretim programı ve kılavuzu [Primary science and technology course curriculum and instruction]. Ankara.
• Ministry of National Education (2006). PISA 2006 ulusal rapor [PISA 2006 national Report]. Retivered from PISA Turkey web site: http://pisa.meb.gov.tr/?page_id=22
• Ministry of National Education (2009). PISA 2009 ulusal rapor [PISA 2009 National Report]. Retivered from PISA Turkey web site: http://pisa.meb.gov.tr/?page_id=22
• Ministry of National Education (2012). PISA 2012 ulusal rapor [PISA 2012 National Report]. Retivered from PISA Turkey web site: http://pisa.meb.gov.tr/?page_id=22
• Ministry of National Education (2013). İlköğretim kurumları fen bilimleri dersi öğretim programı [Primary school institutions science courses curriculum]. Ankara.
• Ministry of National Education (2018). İlköğretim kurumları fen bilimleri dersi öğretim programı [Primary school institutions science courses curriculum]. Ankara.
• Ministry of National Education (2016). PISA 2015 projesi: Ulusal ön rapor [PISA 2015 project: National pre-report]. Retivered from Ankara. http://pisa.meb.gov.tr/wp-content/uploads/2016/12/PISA2015_Ulusal_Rapor.pdf
• Minner, D. D., Levy, A. J., & Century, J. (2010). Inquiry-based science instruction-what is it and does it matter? Results from a research synthesis years 1984 to 2002. Journal of Research in Science Teaching, 47(4), 474– 496.
• Muis, K. R., Bendixen, L. D., & Haerle, F. C. (2006). Domain-generality and domainspecificity in personal epistemology research: Philosophical and empirical reflections in the development of a theoretical framework. Educational Psychology Review, 18(1), 3-54.
• Muthén, L. K., & Muthén, B. O. (2007). Statistical analysis with latent variables using Mplus. Los Angeles, CA: Muthén & Muthén.
• Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. ETS Research Report Series, 1992(1), 1-30.
• Ning, B., Van Damme, J., Van Den Noortgate, W., Yang, X., & Gielen, S. (2015). The influence of classroom disciplinary climate of schools on reading achievement: A cross-country comparative study. School Effectiveness and School Improvement, 26(4), 586-611.
• Next Generation Science Standards (2013). Next generation science standards: for states, by states. Washington: National Academies Press.
• Organization for Economic Co-operation and Development (2015a). Scaling procedures and construct validation of context questionnaire data. Retrieved from http://www.oecd.org
• Organization for Economic Co-operation and Development (2015b). Sample design. Retrieved from http://www.oecd.org
• Özdemir, O. (2010). Fen ve teknoloji öğretmen adaylarının fen okuryazarlığının durumu [The status of science and technology teacher candidates' science literacy]. Türk Fen Eğitimi Dergisi, 7(3), 42-56.
• Özdemir, C. (2017). OECD PISA Türkiye verisi kullanılarak yapılan araştırmaların metodolojik taraması [A methodological review of research using oecd pisa Turkey data]. Eğitim Bilim Toplum, 14(56), 10-27.
• Perry, L. B., & McConney, A. (2010). Does the SES of the school matter? An examination of socioeconomic status and student achievement using PISA 2003. Teachers College Record, 112(4), 1137-1162.
• Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: Sage.
• R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retivered from https://www.R-project.org/.
• Ryder, J. (2001). Identifying science understanding for functional scientific literacy. Studies in Science Education, 36, 1-42.
• Sadıç, A., & Çam, A. (2015). Eight grade students’ epistemological beliefs with pisa success and their scientific literacy. Journal of Computer and Education Research, 3(5), 18-49.
• Shin, J., Lee, H., & Kim, Y. (2009). Student and school factors affecting mathematics achievement: International comparisons between Korea, Japan and the USA. School Psychology International, 30(5), 520-537.
• Snijders, T., & Bosker, R. (1999). Multilevel modeling: An introduction to basic and advanced multilevel modeling. London, Sage.
• Sousa, S., Park, E. J., & Armor, D. J. (2012). Comparing effects of family and school factors on cross-national academic achievement using the 2009 and 2006 PISA surveys. Journal of Comparative Policy Analysis: Research and Practice, 14(5), 449-468.
• Spybrook J, Raudenbush SW, Liu X, & Congdon R. (2006). Optimal design for longitudinal and multilevel research: Documentation for the “Optimal Design” software. University of Michigan, Ann Arbor, MI. [Google Scholar]
• Sun, L., Bradley, K. D., & Akers, K. (2012). A multilevel modelling approach to investigating factors impacting science achievement for secondary school students: PISA Hong Kong sample. International Journal of Science Education, 34(14), 2107-2125.
• Taş, U. E., Arıcı, Ö., Ozarkan, H. B., & Özgürlük, B. (2016). PISA 2015 ulusal raporu. [PISA 2015 national report] Ankara: Milli Eğitim Bakanlığı.
• Tierney, N., Cook, D, McBain, M., & Fay, C. (2018). Naniar: Data Structures, Summaries, and Visualisations for Missing Data. R package version 0.4.0.0. Retivered from https://CRAN.R-project.org/package=naniar
• Topçu, M. S., & Yılmaz Tüzün, Ö. (2009). Elementary students' metacognition and epistemological beliefs considering science achievement, gender and socioeconomic status. Elementary Education Online, 8(3), 676-693

• Tsai, C. (2006). Reinterpreting and reconstructing science: Teachers’ view changes towards the nature of science by courses of science education. Teaching and Teacher Education, 22(3), 363-375.
• Weirich, S., Haag, N., Hecht, M., Böhme, K., Siegle, T., & Lüdtke, O. (2014). Nested multiple imputation in large-scale assessments. Large-scale assessments in education, 2(1), 9. Wickham, H. (2016). Ggplot2: Elegant graphics for data analysis. New York. NY: Springer.
• Wigfield, A., & Eccles, J.S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81.
• Wise, K. C., & Okey, J. R. (1983). A meta-analysis of the effects of various science teaching strategies on achievement. Journal of Research in Science Teaching, 20(5), 419-435.
• Yetişir, M. İ., Batı, K., Kahyaoğlu, M., & Birel, F. K. (2018). Investigation of the relation of disadvantaged students to affective characteristics of science literacy performances]. Ankara University Journal of Faculty of Educational Sciences, 51(1), 143-158.
• Yıldırım, S. (2012). Teacher support, motivation, learning strategy use, and achievement: A multilevel mediation model. The Journal of Experimental Education, 80(2), 150-172.
• Young, M. R. (2005). The motivational effects of the classroom environment in facilitating self-regulated learning. Journal of Marketing Education, 27, 25-4.