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Türkiye’deki Öğrencilerin Fen Okuryazarlığını Etkileyen Faktörler Nelerdir? PISA 2015 Verisine Dayalı Bir Hiyerarşik Doğrusal Modelleme Çalışması

Yıl 2020, Cilt: 35 Sayı: 3, 720 - 732, 31.07.2020

Öz

Bu çalışmanın temel amacı, 2015 yılındaki PISA uygulamasında elde edilen veriden yararlanılarak, Türkiye’deki öğrencilerin fen okuryazarlığını etkileyen öğrenci ve okul düzeyindeki değişkenlerin belirlenmesidir. Böylece öğrencilerin fen okuryazarlığı düzeylerini yordayan değişkenlerden oluşan hiyerarşik bir modelin elde edilmesi hedeflenmiştir. Özellikle 2015 yılındaki PISA uygulamasında Türkiye’nin ortalama başarı düzeyindeki keskin düşüş göz önüne alındığında bu çalışmanın sonuçları daha da büyük önem kazanmaktadır. Bu bağlamda, hem verinin kümelenmiş doğası hem de elde edilen yüksek grup-içi korelasyon katsayısı (ICC) değeri (0,52) nedeniyle bahsi geçen modelin elde edilebilmesi için hiyerarşik doğrusal modelleme (HLM) analizinden yararlanılmıştır. Sonuç olarak, öğrencilerin fen okuryazarlığı seviyesini yordayan, öğrenci düzeyinde dokuz, okul düzeyinde ise dört anlamlı değişkenden oluşan bir model elde edilmiştir. Öğrenci düzeyindeki değişkenler, kişiye, öğrenme süresine ve öğrenme-öğretme sürecine özgü değişkenler olmak üzere üç grupta incelenirken okul düzeyindeki değişkenler ise okul kaynaklarıyla ilgili ve okuldaki öğrenme ortamıyla ilgili değişkenler olarak gruplandırılmıştır. Öğrenci düzeyinde en etkili değişken öğrencilerin “haftalık fen dersi süresi” olurken okul düzeyinde öğrenci başarısını en güçlü yordayan değişken ise “okulun fen bilimlerine özgü kaynakları” olmuştur. Bununla birlikte, öğrencilerin okul dışındaki toplam çalışma süresiyle fen okuryazarlığı seviyeleri arasındaki negatif ilişki bu çalışmada elde edilen ilginç sonuçlardan bir tanesi olarak öne çıkmaktadır.

Destekleyen Kurum

Artvin Çoruh Üniversitesi BAP koordinatörlüğü

Proje Numarası

2016.S34.02.02

Kaynakça

  • Acar, T., & Ögretmen, T. (2012). Analysis of 2006 PISA science performance via multilevel statistical methods. Education and Science, 37(163), 178-189.
  • Anderson, J. O., Milford, T., & Ross, S. P. (2009). Multilevel modeling with HLM: Taking a second look at PISA. In M. C. Shelley II, L. D. Yore, & B. Hand, (Eds.), Quality research in literacy and science education (pp. 263-286). Dordrecht: Springer Netherlands.
  • Åström, M., & Karlsson, K. G. (2012). Using hierarchical linear models to test differences in Swedish results from OECD’s PISA 2003: Integrated and subject-specific science education. Nordic Studies in Science Education, 3(2), 121-131.
  • Beese, J., & Liang, X. (2010). Do resources matter? PISA science achievement comparisons between students in the United States, Canada and Finland. Improving Schools, 13(3), 266-279.
  • Cosgrove, J., & Cunningham, R. (2011). A multilevel model of science achievement of Irish students participating in PISA 2006. The Irish Journal of Education/Iris Eireannach an Oideachais, 39, 57-73.
  • Gormally, C., Brickman, P., & Lutz, M. (2012). Developing a test of scientific literacy skills (TOSLS): Measuring undergraduates’ evaluation of scientific information and arguments. CBE—Life Sciences Education, 11, 364-377.
  • Hurd, P. D. (1958). Science literacy: Its meaning for American schools. Educational Leadership, 16(1), 13-16.
  • Jurecki, K., & Wander, M. C. (2012). Science literacy, critical thinking, and scientific literature: Guidelines for evaluating scientific literature in the classroom. Journal of Geoscience Education, 60(2), 100-105.
  • Laugksch, R. C., & Spargo, P. E. (1996). Development of a pool of scientific literacy test‐items based on selected AAAS literacy goals. Science Education, 80(2), 121-143.
  • Liu, X. (2009). Beyond science literacy: Science and the public. International Journal of Environmental and Science Education, 4(3), 301-311.
  • MEB (2016). Uluslararası öğrenci değerlendirme programı PISA 2015 ulusal raporu. MEB Yayınları, Ankara.
  • Miller, J. D. (1983). Scientific literacy: A conceptual and empirical review. Daedalus, 112(2), 29-48.
  • Morgan, H. (2014). Review of research: The education system in Finland: A success story other countries can emulate, Childhood Education, 90(6), 453-457.
  • OECD (2012). PISA 2009 technical report. Paris: OECD Publishing.
  • OECD (2016a). PISA 2015 results (Volume I): Excellence and equity in education. Paris: OECD Publishing.
  • OECD (2016b). PISA 2015 results (Volume II): Policies and practices for successful schools. Paris: OECD Publishing.
  • OECD (2017). PISA 2015 results (Volume V): Collaborative problem solving. Paris: OECD Publishing.
  • Ross, K., Hooten, M. A., & Cohen, G. (2013). Promoting science literacy through an interdisciplinary approach. Bioscene: Journal of College Biology Teaching, 39(1), 21-26.
  • Rutherford, F. J., & Ahlgren, A. (1991). Science for all Americans. New York: Oxford University Press.
  • Sahlberg, P. (2015). Finnish lessons 2.0: What can the world learn from educational change in Finland? New York: Teachers College Press.
  • Seraphin, K. D. (2014). Where are you from? Writing toward science literacy by connecting culture, person, and place. Journal of Geoscience Education, 62(1), 11-18.
  • Ustun, U., & Eryilmaz, A. (2018). Analysis of Finnish Education System to question the reasons behind Finnish success in PISA. Studies in Educational Research and Development, 2(2), 93-114.
  • Zhang, D., & Liu, L. (2016). How does ICT use influence students’ achievements in math and science over time? Evidence from PISA 2000 to 2012. Eurasia Journal of Mathematics, Science & Technology Education, 12(9), 2431-2449.

What are the Factors Affecting Turkish Students’ Science Literacy? A Hierarchical Linear Modelling Study Using PISA 2015 Data

Yıl 2020, Cilt: 35 Sayı: 3, 720 - 732, 31.07.2020

Öz

The main purpose of this study is to investigate the student and school-level variables affecting Turkish students’ science literacy using PISA 2015 data. In this way, we aim to build a hierarchical model of the variables predicting students’ science literacy level. Particularly, when we consider the sharp decrease in Turkish students’ success in PISA 2015, the implications of this study would be even stronger. Because of the nested nature of the data and a high intraclass correlation coefficient (ICC) value (0.52), we performed hierarchical linear modeling (HLM) analysis. As a result, we constructed a model including nine student-level and four school-level variables to predict students’ science literacy scores. We classified the student-level variables into three categories as personal characteristics, variables associated with learning time, and variables associated with teaching-learning process. Similarly, we classified the school-level variables into two categories: school resources and learning environment in the school. While “weekly science learning time” is the most prominent variable at the student-level, “science specific resources”, at the school-level, seems to be the most powerful predictor of students’ success. One of the surprising findings in this study is that there is a significant negative correlation between “out-of-school study time” and science literacy scores.

Proje Numarası

2016.S34.02.02

Kaynakça

  • Acar, T., & Ögretmen, T. (2012). Analysis of 2006 PISA science performance via multilevel statistical methods. Education and Science, 37(163), 178-189.
  • Anderson, J. O., Milford, T., & Ross, S. P. (2009). Multilevel modeling with HLM: Taking a second look at PISA. In M. C. Shelley II, L. D. Yore, & B. Hand, (Eds.), Quality research in literacy and science education (pp. 263-286). Dordrecht: Springer Netherlands.
  • Åström, M., & Karlsson, K. G. (2012). Using hierarchical linear models to test differences in Swedish results from OECD’s PISA 2003: Integrated and subject-specific science education. Nordic Studies in Science Education, 3(2), 121-131.
  • Beese, J., & Liang, X. (2010). Do resources matter? PISA science achievement comparisons between students in the United States, Canada and Finland. Improving Schools, 13(3), 266-279.
  • Cosgrove, J., & Cunningham, R. (2011). A multilevel model of science achievement of Irish students participating in PISA 2006. The Irish Journal of Education/Iris Eireannach an Oideachais, 39, 57-73.
  • Gormally, C., Brickman, P., & Lutz, M. (2012). Developing a test of scientific literacy skills (TOSLS): Measuring undergraduates’ evaluation of scientific information and arguments. CBE—Life Sciences Education, 11, 364-377.
  • Hurd, P. D. (1958). Science literacy: Its meaning for American schools. Educational Leadership, 16(1), 13-16.
  • Jurecki, K., & Wander, M. C. (2012). Science literacy, critical thinking, and scientific literature: Guidelines for evaluating scientific literature in the classroom. Journal of Geoscience Education, 60(2), 100-105.
  • Laugksch, R. C., & Spargo, P. E. (1996). Development of a pool of scientific literacy test‐items based on selected AAAS literacy goals. Science Education, 80(2), 121-143.
  • Liu, X. (2009). Beyond science literacy: Science and the public. International Journal of Environmental and Science Education, 4(3), 301-311.
  • MEB (2016). Uluslararası öğrenci değerlendirme programı PISA 2015 ulusal raporu. MEB Yayınları, Ankara.
  • Miller, J. D. (1983). Scientific literacy: A conceptual and empirical review. Daedalus, 112(2), 29-48.
  • Morgan, H. (2014). Review of research: The education system in Finland: A success story other countries can emulate, Childhood Education, 90(6), 453-457.
  • OECD (2012). PISA 2009 technical report. Paris: OECD Publishing.
  • OECD (2016a). PISA 2015 results (Volume I): Excellence and equity in education. Paris: OECD Publishing.
  • OECD (2016b). PISA 2015 results (Volume II): Policies and practices for successful schools. Paris: OECD Publishing.
  • OECD (2017). PISA 2015 results (Volume V): Collaborative problem solving. Paris: OECD Publishing.
  • Ross, K., Hooten, M. A., & Cohen, G. (2013). Promoting science literacy through an interdisciplinary approach. Bioscene: Journal of College Biology Teaching, 39(1), 21-26.
  • Rutherford, F. J., & Ahlgren, A. (1991). Science for all Americans. New York: Oxford University Press.
  • Sahlberg, P. (2015). Finnish lessons 2.0: What can the world learn from educational change in Finland? New York: Teachers College Press.
  • Seraphin, K. D. (2014). Where are you from? Writing toward science literacy by connecting culture, person, and place. Journal of Geoscience Education, 62(1), 11-18.
  • Ustun, U., & Eryilmaz, A. (2018). Analysis of Finnish Education System to question the reasons behind Finnish success in PISA. Studies in Educational Research and Development, 2(2), 93-114.
  • Zhang, D., & Liu, L. (2016). How does ICT use influence students’ achievements in math and science over time? Evidence from PISA 2000 to 2012. Eurasia Journal of Mathematics, Science & Technology Education, 12(9), 2431-2449.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Alan Eğitimleri
Bölüm Makaleler
Yazarlar

Ulaş Üstün 0000-0001-9974-6897

Ertuğrul Özdemir 0000-0002-6057-5944

Mustafa Cansız Bu kişi benim 0000-0002-7157-2888

Nurcan Cansız 0000-0002-2336-3205

Proje Numarası 2016.S34.02.02
Yayımlanma Tarihi 31 Temmuz 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 35 Sayı: 3

Kaynak Göster

APA Üstün, U., Özdemir, E., Cansız, M., Cansız, N. (2020). Türkiye’deki Öğrencilerin Fen Okuryazarlığını Etkileyen Faktörler Nelerdir? PISA 2015 Verisine Dayalı Bir Hiyerarşik Doğrusal Modelleme Çalışması. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 35(3), 720-732.