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A Comparative Analysis of PISA 2015 Türkiye Studies: Introducing A Variable Selection Model to International Large-Scale Assessments

Yıl 2024, Cilt: 21 Sayı: 3, 840 - 868
https://doi.org/10.33711/yyuefd.1539072

Öz

International large-scale assessments have a key role in improving educational, economical, and political systems. By using the data of these assessments, countries can draw conclusions about the status of educational systems. Studies and reports generally tend to choose variables available in data set to model the relationships among the variables. In this study, we aimed to introduce a variable selection method to analyze large-scale assessments to be able to decide which variables might be included in modelling country data. We used the entire data set of Türkiye PISA 2015 through elastic net regression to decide which variables should be selected for further analysis. We also provided a summary of the available studies based on Türkiye PISA 2015 data and compared the results. Based on the series of analyses, this study revealed that test anxiety, environmental awareness, interest in broad topics in science, playing video games after school, mathematics literacy, reading literacy, and collaborative problem-solving skills were the explanatory variables contributed most to the degree of scientific literacy of students. This study has a potential to provide an example of shrinkage methods applied in educational context and offer another standpoint for providing a rationale to select which variables can be included.

Kaynakça

  • Akgenç, E. & Yapıcı Pehlivan, N. (2019). Analysis of PISA-2015 performance of Turkish students by multilevel structural equation modeling. Mugla Journal of Science and Technology, 5(1), 43–51. https://doi.org/10.22531/muglajsci.484469
  • Arıkan, S., Yıldırım, K., & Erbilgin, E. (2017). Exploring the relationship among new literacies, reading, mathematics and science performance of Turkish students in PISA 2012. International Electronic Journal of Elementary Education, 8(4), 573–588. https://www.iejee.com/index.php/IEJEE/article/view/133
  • Bybee, R. W. (2010). What is STEM education? Science, 329(5995), 996. https://doi.org/10.1126/science.1194998
  • Carter, L. (2008). Sociocultural influences on science education: Innovation for contemporary times. Science Education, 92(1), 165–181. https://doi.org/10.1002/sce.20228
  • Chang, C. Y., & Cheng, W. Y. (2008). Science achievement and students’ self‐confidence and interest in science: A Taiwanese representative sample study. International Journal of Science Education, 30(9), 1183–1200. https://doi.org/10.1080/09500690701435384
  • Chaarani, B., Ortigara, J., Yuan, D., Loso, H., Potter, A., & Garavan, H. P. (2022). Association of video gaming with cognitive performance among children. JAMA Network Open, 5(10), e2235721. https://doi.org/10.1001/jamanetworkopen.2022.35721
  • Choi, K., Lee, H., Shin, N., Kim, S. W., & Krajcik, J. (2011). Re‐conceptualization of scientific literacy in South Korea for the 21st century. Journal of Research in Science Teaching, 48(6), 670–697. https://doi.org/10.1002/tea.20424
  • Coll, R.K., Taylor, N. (2012). An international perspective on science curriculum development and implementation. In Fraser, B., Tobin, K., McRobbie, C. (Eds.) Second international handbook of science education. Springer. https://doi.org/10.1007/978-1-4020-9041-7_51
  • Demirci, S. (2018). A Closer look to Turkish students' scientific literacy: what do pisa 2015 results tell us? [Master's thesis, Middle East Technical University].
  • Dolu, A. (2020). Sosyoekonomik faktörlerin eğitim performansı üzerine etkisi: PISA 2015 Türkiye örneği. Journal of Management and Economics Research, 18(2), 41–58. https://doi.org/10.11611/yead.607838
  • Erbas, A. K., Tuncer Teksoz, G., & Tekkaya, C. (2012). An Evaluation of Environmental Responsibility and Its Associated Factors: Reflections from PISA 2006. Eurasian Journal of Educational Research, 46, 41–62. https://eric.ed.gov/?id=EJ1057292
  • Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.
  • Friedman, J. Hastie, T., Tibshirani, R., Simon, N., Narasimhan, B. & Qian, J. (2018). Lasso and Elastic-Net Regularized Generalized Linear Models. https://cran.r-project.org/web/packages/glmnet/glmnet.pdf
  • Genc, A. (2017). Coping strategies as mediators in the relationship between test anxiety and academic achievement. Psihologija, 50(1), 51–66. https://doi.org/10.2298/PSI160720005G
  • Grabau, L. J., & Ma, X. (2017). Science engagement and science achievement in the context of science instruction: a multilevel analysis of US students and schools. International Journal of Science Education, 39(8), 1045–1068. https://doi.org/10.1080/09500693.2017.1313468
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Springer.
  • Hadzigeorgiou, Y., & Skoumios, M. (2013). The development of environmental awareness through school science: problems and possibilities. International Journal of Environmental and Science Education, 8(3), 405–426. https://files.eric.ed.gov/fulltext/EJ1016851.pdf
  • Haşıloğlu, M. A. & Göğebakan, S. (2021). Ortaokul 8. sınıf öğrencilerinin fen bilimleri dersine yönelik kaygılarının bazı değişkenler açısından incelenmesi. Fen Matematik Girişimcilik ve Teknoloji Eğitimi Dergisi, 4(2), 141–154. https://dergipark.org.tr/en/pub/fmgted/issue/62218/888624
  • IEA Data Processing and Research Center (2018). New Features and Installation Guide for the IEA’s IDB Analyzer (Version 3.2). https://www.iea.nl/fileadmin/user_upload/IEA_Software/Help_Manual_for_the_IDB_Analyzer__Version_3_2__-_Features_and_Install_Guide_01.pdf
  • Karslı, N., Berberoğlu, G. & Çalışkan, M. (2019). Türkiye’de PISA fen okuryazarlık puanlarını yordayan değişkenler. Uluslararası Bilim ve Eğitim Dergisi, 2 (2), 38–49. https://dergipark.org.tr/en/pub/ubed/issue/50464/567861
  • Kaya, V. H. (2017). Okuma becerilerinin fen bilimleri okuryazarlığına etkisi. Milli Eğitim Dergisi, 46(215), 193–207. https://dergipark.org.tr/en/pub/milliegitim/issue/36134/405905
  • Kullman, D. E. (1966). Correlation of mathematics and science teaching. School Science and Mathematics, 66(7), 645–649. https://doi.org/10.1111/j.1949-8594.1966.tb13617.x
  • Laugksch, R. C. (2000). Scientific literacy: A conceptual overview. Science Education, 84(1), 71–94.https://doi.org/10.1002/(SICI)1098-237X(200001)84:1%3C71::AID-SCE6%3E3.0.CO;2-C
  • Lewis, J. D. (1982). Technology, enterprise, and American economic growth. Science, 215(4537),1204–1211. https://doi.org/10.1126/science.215.4537.1204
  • Ministry of National Education [MONE] (2018). PISA 2015 ulusal raporu. http://pisa.meb.gov.tr/wp-content/uploads/2014/11/PISA2015_UlusalRapor.pdf
  • Organisation for Economic Co-Operation and Development [OECD] (2009). PISA Data Analysis Manual SPSS, Second Edition. http://archivos.agenciaeducacion.cl/Manual_de_Analisis_de_datos_SPSS_version_ingles.pdf
  • Organisation for Economic Co-Operation and Development [OECD] (2016). PISA 2015 Results (Volume I) Excellence and Equity in Education. OECD Publishing. http://dx.doi.org/10.1787/9789264266490-en
  • Organisation for Economic Co-Operation and Development [OECD] (2017). Education at a Glance 2017: OECD Indicators. OECD Publishing. http://dx.doi.org/10.1787/eag-2017-en
  • Öztürk, Ö. (2018). Using PISA 2015 data to analyze how the scientific literacy of students from different socioeconomic levels can be predicted by environmental awareness and by environmental optimism (Publication No. 29045663). [Master’s Thesis, Ihsan Dogramaci Bilkent University]. ProQuest Dissertations and Theses Global.
  • Palincsar, A. S., Anderson, C., & David, Y. M. (1993). Pursuing scientific literacy in the middle grades through collaborative problem solving. The Elementary School Journal, 93(5), 643–658. https://doi.org/10.1086/461745
  • Pedretti E. (2014). Environmental education and science education: ideology, hegemony, traditional knowledge, and alignment. Revista Brasileira de Pesquisa em Educação em Ciências, 14(2), 305–314.
  • Pedretti, E., & Nazir, J. (2011). Currents in STSE education: Mapping a complex field, 40 years on. Science Education, 95(4), 601–626.
  • R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/.
  • Roberts, D. (2007). Scientific literacy/science literacy. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 729–780). Mahwah, NJ: Lawrence Erlbaum Associates.
  • Roberts, D. A., & Bybee, R. W. (2014). Scientific literacy, science literacy, and science education. In Lederman N. G. & S. K. Abell (Eds.), Handbook of research on science education, Volume II (pp. 559–572). Routledge. https://doi.org/10.4324/9780203097267
  • Santi, V. M., Notodiputro, K. A., & Sartono, B. (2019, December). Variable selection methods applied to the mathematics scores of Indonesian students based on convex penalized likelihood. In Journal of Physics: Conference Series (Vol. 1402, No. 7, p. 077096). IOP Publishing. https://doi.org/10.1088/1742-6596/1402/7/077096
  • Solomon, J., & Aikenhead, G. (1994). STS Education: International Perspectives on Reform. Ways of Knowing Science Series. Teachers College Press, 1234 Amsterdam Ave., New York, NY 10027 (clothbound: ISBN-0-8077-3366-0; paperback: ISBN-0-8077-3365-2).
  • Tat, O., Koyuncu, İ. & Gelbal, S. (2019). The influence of using plausible values and survey weights on multiple regression and hierarchical linear model parameters. Journal of Measurement and Evaluation in Education and Psychology, 10(3), 235–248. https://doi.org/10.21031/epod.486999
  • Tu, Y. K., Gunnell, D., & Gilthorpe, M. S. (2008). Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon–the reversal paradox. Emerging Themes in Epidemiology, 5(1), 1–9. https://doi.org/10.1186/1742-7622-5-2
  • 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. https://doi.org/10.16986/HUJE.2019050786
  • Valladares, L. (2021). Scientific literacy and social transformation. Science & Education, 30(3), 557–587. https://doi.org/10.1007/s11191-021-00205-2
  • Wals, A. (2011). Learning our way to sustainability. Journal of Education for Sustainable Development, 5, 177–86. https://doi.org/10.1177/097340821100500208
  • Wright, B. D., & Masters, G. N. (1982). Rating scale analysis: Rasch measurement. Mesa Press.
  • Yetişir, M. İ., & Kaan, B. A. T. I. (2021). The effect of school and student-related factors on PISA 2015 science performances in Türkiye. International Journal of Psychology and Educational Studies, 8(2), 170–186. https://dergipark.org.tr/en/pub/pes/issue/62298/935968
  • Yıldız, M., Erdas Kartal, E., & Mesci, G. (2020). Investigation of Türkiye's PISA 2015 Science Performance and Associated Variables Using Hierarchical Linear Modeling. Necatibey Faculty of Education Electronic Journal of Science & Mathematics Education, 14(1). https://doi.org/10.17522/balikesirnef.663737
  • Young, M. F., Slota, S., Cutter, A. B., Jalette, G., Mullin, G., Lai, B., ... & Yukhymenko, M. (2012). Our princess is in another castle: A review of trends in serious gaming for education. Review of Educational Research, 82(1), 61–89. https://doi.org/10.3102/0034654312436980
  • Zeidler, D. L., Sadler, T. D., Applebaum, S., & Callahan, B. E. (2009). Advancing reflective judgment through socioscientific issues. Journal of Research in Science Teaching, 46(1), 74–101.
  • Zou, H., & Hastie, T. (2005). Regression shrinkage and selection via the elastic net, with applications to microarrays. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00527.x

PISA 2015 Türkiye Çalışmalarının Karşılaştırmalı Bir İncelemesi: Uluslararası Büyük Ölçekli Değerlendirmeler için Değişken Seçim Yöntemi Önerisi

Yıl 2024, Cilt: 21 Sayı: 3, 840 - 868
https://doi.org/10.33711/yyuefd.1539072

Öz

Uluslararası büyük ölçekli değerlendirmeler, eğitim, ekonomik ve politik sistemlerin iyileştirilmesinde önemli bir rol oynamaktadır. Ülkeler, bu değerlendirmelerin verilerini kullanarak, eğitim sistemlerinin mevcut durumu hakkında çıkarımlarda bulunmaktadır. Bu verileri kullanan bilimsel çalışmalar ve raporlar, genellikle veri setinde mevcut olan bazı değişkenleri seçerek bu değişkenler arasındaki ilişkileri modellemeyi amaçlar. Bu çalışmada, Türkiye PISA 2015 verisinin tamamını kullanarak ülke verilerini modellemede hangi değişkenlerin dahil edilebileceğine karar vermek amacıyla bir değişken seçim yöntemi denemeyi hedeflenmiştir. PISA 2015 verisinin tamamını kullanarak büzüşme regresyonlarından biri olan elastik net regresyonu kullanılmış ve elde edilen sonuçlar, Türkiye PISA 2015 verilerine dayalı mevcut çalışmaların sonuçları ile karşılaştırılmıştır. Analizler sonucunda, test kaygısı, çevresel farkındalık, geniş kapsamlı bilim konularına ilgi, okul sonrası video oyunları oynama, matematik okuryazarlığı, okuma okuryazarlığı ve işbirlikçi problem çözme becerilerinin öğrencilerin fen okuryazarlığı düzeyine en çok katkı sağlayan açıklayıcı değişkenler olduğu ortaya konulmuştur. Bu çalışma, eğitim bağlamında küçülme yöntemlerinin uygulanmasına bir örnek sunma potansiyeline sahip olup, hangi değişkenlerin dahil edilebileceğine yönelik bir gerekçe sunmak için alternatif bir bakış açısı önermektedir.

Kaynakça

  • Akgenç, E. & Yapıcı Pehlivan, N. (2019). Analysis of PISA-2015 performance of Turkish students by multilevel structural equation modeling. Mugla Journal of Science and Technology, 5(1), 43–51. https://doi.org/10.22531/muglajsci.484469
  • Arıkan, S., Yıldırım, K., & Erbilgin, E. (2017). Exploring the relationship among new literacies, reading, mathematics and science performance of Turkish students in PISA 2012. International Electronic Journal of Elementary Education, 8(4), 573–588. https://www.iejee.com/index.php/IEJEE/article/view/133
  • Bybee, R. W. (2010). What is STEM education? Science, 329(5995), 996. https://doi.org/10.1126/science.1194998
  • Carter, L. (2008). Sociocultural influences on science education: Innovation for contemporary times. Science Education, 92(1), 165–181. https://doi.org/10.1002/sce.20228
  • Chang, C. Y., & Cheng, W. Y. (2008). Science achievement and students’ self‐confidence and interest in science: A Taiwanese representative sample study. International Journal of Science Education, 30(9), 1183–1200. https://doi.org/10.1080/09500690701435384
  • Chaarani, B., Ortigara, J., Yuan, D., Loso, H., Potter, A., & Garavan, H. P. (2022). Association of video gaming with cognitive performance among children. JAMA Network Open, 5(10), e2235721. https://doi.org/10.1001/jamanetworkopen.2022.35721
  • Choi, K., Lee, H., Shin, N., Kim, S. W., & Krajcik, J. (2011). Re‐conceptualization of scientific literacy in South Korea for the 21st century. Journal of Research in Science Teaching, 48(6), 670–697. https://doi.org/10.1002/tea.20424
  • Coll, R.K., Taylor, N. (2012). An international perspective on science curriculum development and implementation. In Fraser, B., Tobin, K., McRobbie, C. (Eds.) Second international handbook of science education. Springer. https://doi.org/10.1007/978-1-4020-9041-7_51
  • Demirci, S. (2018). A Closer look to Turkish students' scientific literacy: what do pisa 2015 results tell us? [Master's thesis, Middle East Technical University].
  • Dolu, A. (2020). Sosyoekonomik faktörlerin eğitim performansı üzerine etkisi: PISA 2015 Türkiye örneği. Journal of Management and Economics Research, 18(2), 41–58. https://doi.org/10.11611/yead.607838
  • Erbas, A. K., Tuncer Teksoz, G., & Tekkaya, C. (2012). An Evaluation of Environmental Responsibility and Its Associated Factors: Reflections from PISA 2006. Eurasian Journal of Educational Research, 46, 41–62. https://eric.ed.gov/?id=EJ1057292
  • Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.
  • Friedman, J. Hastie, T., Tibshirani, R., Simon, N., Narasimhan, B. & Qian, J. (2018). Lasso and Elastic-Net Regularized Generalized Linear Models. https://cran.r-project.org/web/packages/glmnet/glmnet.pdf
  • Genc, A. (2017). Coping strategies as mediators in the relationship between test anxiety and academic achievement. Psihologija, 50(1), 51–66. https://doi.org/10.2298/PSI160720005G
  • Grabau, L. J., & Ma, X. (2017). Science engagement and science achievement in the context of science instruction: a multilevel analysis of US students and schools. International Journal of Science Education, 39(8), 1045–1068. https://doi.org/10.1080/09500693.2017.1313468
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Springer.
  • Hadzigeorgiou, Y., & Skoumios, M. (2013). The development of environmental awareness through school science: problems and possibilities. International Journal of Environmental and Science Education, 8(3), 405–426. https://files.eric.ed.gov/fulltext/EJ1016851.pdf
  • Haşıloğlu, M. A. & Göğebakan, S. (2021). Ortaokul 8. sınıf öğrencilerinin fen bilimleri dersine yönelik kaygılarının bazı değişkenler açısından incelenmesi. Fen Matematik Girişimcilik ve Teknoloji Eğitimi Dergisi, 4(2), 141–154. https://dergipark.org.tr/en/pub/fmgted/issue/62218/888624
  • IEA Data Processing and Research Center (2018). New Features and Installation Guide for the IEA’s IDB Analyzer (Version 3.2). https://www.iea.nl/fileadmin/user_upload/IEA_Software/Help_Manual_for_the_IDB_Analyzer__Version_3_2__-_Features_and_Install_Guide_01.pdf
  • Karslı, N., Berberoğlu, G. & Çalışkan, M. (2019). Türkiye’de PISA fen okuryazarlık puanlarını yordayan değişkenler. Uluslararası Bilim ve Eğitim Dergisi, 2 (2), 38–49. https://dergipark.org.tr/en/pub/ubed/issue/50464/567861
  • Kaya, V. H. (2017). Okuma becerilerinin fen bilimleri okuryazarlığına etkisi. Milli Eğitim Dergisi, 46(215), 193–207. https://dergipark.org.tr/en/pub/milliegitim/issue/36134/405905
  • Kullman, D. E. (1966). Correlation of mathematics and science teaching. School Science and Mathematics, 66(7), 645–649. https://doi.org/10.1111/j.1949-8594.1966.tb13617.x
  • Laugksch, R. C. (2000). Scientific literacy: A conceptual overview. Science Education, 84(1), 71–94.https://doi.org/10.1002/(SICI)1098-237X(200001)84:1%3C71::AID-SCE6%3E3.0.CO;2-C
  • Lewis, J. D. (1982). Technology, enterprise, and American economic growth. Science, 215(4537),1204–1211. https://doi.org/10.1126/science.215.4537.1204
  • Ministry of National Education [MONE] (2018). PISA 2015 ulusal raporu. http://pisa.meb.gov.tr/wp-content/uploads/2014/11/PISA2015_UlusalRapor.pdf
  • Organisation for Economic Co-Operation and Development [OECD] (2009). PISA Data Analysis Manual SPSS, Second Edition. http://archivos.agenciaeducacion.cl/Manual_de_Analisis_de_datos_SPSS_version_ingles.pdf
  • Organisation for Economic Co-Operation and Development [OECD] (2016). PISA 2015 Results (Volume I) Excellence and Equity in Education. OECD Publishing. http://dx.doi.org/10.1787/9789264266490-en
  • Organisation for Economic Co-Operation and Development [OECD] (2017). Education at a Glance 2017: OECD Indicators. OECD Publishing. http://dx.doi.org/10.1787/eag-2017-en
  • Öztürk, Ö. (2018). Using PISA 2015 data to analyze how the scientific literacy of students from different socioeconomic levels can be predicted by environmental awareness and by environmental optimism (Publication No. 29045663). [Master’s Thesis, Ihsan Dogramaci Bilkent University]. ProQuest Dissertations and Theses Global.
  • Palincsar, A. S., Anderson, C., & David, Y. M. (1993). Pursuing scientific literacy in the middle grades through collaborative problem solving. The Elementary School Journal, 93(5), 643–658. https://doi.org/10.1086/461745
  • Pedretti E. (2014). Environmental education and science education: ideology, hegemony, traditional knowledge, and alignment. Revista Brasileira de Pesquisa em Educação em Ciências, 14(2), 305–314.
  • Pedretti, E., & Nazir, J. (2011). Currents in STSE education: Mapping a complex field, 40 years on. Science Education, 95(4), 601–626.
  • R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/.
  • Roberts, D. (2007). Scientific literacy/science literacy. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 729–780). Mahwah, NJ: Lawrence Erlbaum Associates.
  • Roberts, D. A., & Bybee, R. W. (2014). Scientific literacy, science literacy, and science education. In Lederman N. G. & S. K. Abell (Eds.), Handbook of research on science education, Volume II (pp. 559–572). Routledge. https://doi.org/10.4324/9780203097267
  • Santi, V. M., Notodiputro, K. A., & Sartono, B. (2019, December). Variable selection methods applied to the mathematics scores of Indonesian students based on convex penalized likelihood. In Journal of Physics: Conference Series (Vol. 1402, No. 7, p. 077096). IOP Publishing. https://doi.org/10.1088/1742-6596/1402/7/077096
  • Solomon, J., & Aikenhead, G. (1994). STS Education: International Perspectives on Reform. Ways of Knowing Science Series. Teachers College Press, 1234 Amsterdam Ave., New York, NY 10027 (clothbound: ISBN-0-8077-3366-0; paperback: ISBN-0-8077-3365-2).
  • Tat, O., Koyuncu, İ. & Gelbal, S. (2019). The influence of using plausible values and survey weights on multiple regression and hierarchical linear model parameters. Journal of Measurement and Evaluation in Education and Psychology, 10(3), 235–248. https://doi.org/10.21031/epod.486999
  • Tu, Y. K., Gunnell, D., & Gilthorpe, M. S. (2008). Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon–the reversal paradox. Emerging Themes in Epidemiology, 5(1), 1–9. https://doi.org/10.1186/1742-7622-5-2
  • 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. https://doi.org/10.16986/HUJE.2019050786
  • Valladares, L. (2021). Scientific literacy and social transformation. Science & Education, 30(3), 557–587. https://doi.org/10.1007/s11191-021-00205-2
  • Wals, A. (2011). Learning our way to sustainability. Journal of Education for Sustainable Development, 5, 177–86. https://doi.org/10.1177/097340821100500208
  • Wright, B. D., & Masters, G. N. (1982). Rating scale analysis: Rasch measurement. Mesa Press.
  • Yetişir, M. İ., & Kaan, B. A. T. I. (2021). The effect of school and student-related factors on PISA 2015 science performances in Türkiye. International Journal of Psychology and Educational Studies, 8(2), 170–186. https://dergipark.org.tr/en/pub/pes/issue/62298/935968
  • Yıldız, M., Erdas Kartal, E., & Mesci, G. (2020). Investigation of Türkiye's PISA 2015 Science Performance and Associated Variables Using Hierarchical Linear Modeling. Necatibey Faculty of Education Electronic Journal of Science & Mathematics Education, 14(1). https://doi.org/10.17522/balikesirnef.663737
  • Young, M. F., Slota, S., Cutter, A. B., Jalette, G., Mullin, G., Lai, B., ... & Yukhymenko, M. (2012). Our princess is in another castle: A review of trends in serious gaming for education. Review of Educational Research, 82(1), 61–89. https://doi.org/10.3102/0034654312436980
  • Zeidler, D. L., Sadler, T. D., Applebaum, S., & Callahan, B. E. (2009). Advancing reflective judgment through socioscientific issues. Journal of Research in Science Teaching, 46(1), 74–101.
  • Zou, H., & Hastie, T. (2005). Regression shrinkage and selection via the elastic net, with applications to microarrays. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00527.x
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Fen Bilgisi Eğitimi
Bölüm Makaleler
Yazarlar

Sinem Demirci 0000-0002-2095-0674

Özlem İlk 0000-0002-4179-0534

Erken Görünüm Tarihi 22 Aralık 2024
Yayımlanma Tarihi
Gönderilme Tarihi 26 Ağustos 2024
Kabul Tarihi 2 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 21 Sayı: 3

Kaynak Göster

APA Demirci, S., & İlk, Ö. (2024). A Comparative Analysis of PISA 2015 Türkiye Studies: Introducing A Variable Selection Model to International Large-Scale Assessments. Van Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 21(3), 840-868. https://doi.org/10.33711/yyuefd.1539072