The multilevel effects of student and classroom factors on the science achievement of eighth graders in Turkey

Bu çalışma, Türkiye’deki 8. sınıf öğrencilerinin fen başarısını öğrenci ve sınıf düzeyindeki değişkenlerle ilişkisi bakımından incelemekte; bu değişkenler arasındaki ilişkiyi modellemektedir. Bu amaçla TIMSS 2011 uygulamasından elde edilen veriler HLM (hierarchical linear model) kullanılarak analiz edilmiştir. Yapılan analizler sonucunda sekizinci sınıflar arasında fen başarı varyansının %32 olup istatistiksel olarak anlamlı olduğu ortaya çıkmıştır. Çalışmada ayrıca, fene ilişkin tutum ve ebeveynlerin eğitim durumunun fen başarısı ile pozitif yönde bir ilişkisi olduğu; ancak öğrencilerin derse katılımı ile fen başarıları arasında anlamlı bir ilişkinin olmadığı bulgusuna ulaşılmıştır. Yapılan analizler sonucunda, öğretmen işbirliğinin ve araştırmaya dayalı etkinliklerin fen başarısı üzerinde istatistiksel olarak anlamlı bir etkisi bulunmazken; öğrencilerin derse katılımına ilişkin sınıf ortalamasının ve öğrenmeye hazır bulunuşluğun fen başarısı üzerinde anlamlı bir etkisi olduğu ortaya çıkmıştır.

Türkiye'de sekizinci sınıf öğrencilerinin fen başarısına öğrenci ve sınıf faktörlerinin çok düzeyli etkileri

This study investigated the science achievement of eighth graders in Turkey in terms of the relationship of science achievement with the selected student- and classroom-level variables, and modeled the relationship among these variables. The TIMSS 2011 data were used for this purpose. A hierarchical linear model was used to analyze the data. The results of the analysis revealed that the variance in science achievement among eighth- grade classrooms is statistically significant. The variance is about 32%. The result also showed that while attitudes towards science and parents’ level of education are positively related to science achievement, student engagement has no relation with science achievement. Furthermore, the analysis showed that while teacher collaboration and inquiry-related activities do not have a statistically significant effect; class average-engagement and readiness to learn have a significant effect on science achievement.

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  • Adams, J. (2000). Taking Charge of the Curriculum. New York: Teachers College Press.
  • Arnaud, C. (2004). Parental education and child’s education: A natural experiment. IZA Discussion Paper No. 1153, Institute for the Study of Labor, Germany.
  • Atar, H. Y. & Atar, B. (2012). Investigating the Multilevel Effects of Several Variables on Turkish Students’ Science Achievements on TIMSS. Journal of Baltic Science Education, 11(2), 115-126.
  • Avalos, B. (1998). School-based teacher development: The experience of teacher professional groups in secondary school s in Chile. Teaching and Teacher Education, 14 (3), 257-271.
  • Bolam, R., McMahon, A., Stoll, L., Thomas, S., Wallace, M., Greenwood, A., Hawkey, K., Ingram, M., Atkinson, A. & Smith, M. (2005). Creating and sustaining effective professional learning communities. Research Report 637. London: DfES and University of Bristol.
  • Brown, L. J., Beardslee, W. H., & Prothrow, D. ( 2008). Impact of school breakfast on children’s health and learning: An analysis of the scientific research. Unpublished Manuscript. Harvard School of Public Health. Retrieved June 21, 2013 from
  • Ceylan, E. & Berberoglu, G. (2007). Öğrencilerin Fen Başarısını Açıklayan Etmenler: Bir Modelleme Çalışması. Eğitim ve Bilim, 32(144), 36-48.
  • Chang, M., Singh, K., & Mo, Y. (2007). Science engagement and science achievement: Longitudinal models using NELS data. Educational Research and Evaluation, 13(4), 349–371.
  • Chepete, P., (2008). Modeling of the Factors Affecting Mathematical Achievement of Form 1 Students in Botswana Based on the 2003 Trends in International Mathematics and Science Study. PhD study. Indiana University Bloomington, IN.
  • Curcio, G., Ferrara, M., & Gennaro, L. D. (2006). Sleep loss, learning capacity and academic performance. Sleep Medicine Reviews, 10, 323–337.
  • Davis-Kean, P. E. (2005). The Influence of Parent Education and Family Income on Child Achievement: The Indirect Role of Parental Expectations and the Home Environment. Journal of Family Psychology, 19(2), 294–304.
  • Fredericks, 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.
  • Hamilton, L. et al. (2003). Studying large-scale reforms of instructional practice: an example from mathematics and science. Educational Evaluation and Policy Analysis, 25 (1), 1–29.
  • Hammouri, H.A.M. (2004). Attitudinal and motivational variables related to mathematics achievement in Jordan: Findings from the third international mathematics and science study. Educational Research, 46(3), 214-257.
  • Hargreaves, A. (2007). Sustainable professional learning communities. In L. Stoll & K. Seashore Louis (Eds.), Professional learning communities: Divergence, depth and dilemmas. Berkshire, England: Open University Press.
  • Haveman, R., & Wolfe, B. (1995). The determinants of children’s attainments: A review of methods and findings. Journal of Economic Literature, 33, 1829–1878.
  • House, J. D. (2000). Relationships between instructional activities and science achievement of adolescent students in Hong Kong: Finding from the third international mathematics and science study (TIMSS). Studies in Educational Evaluation, 27, 275–289.
  • House, J. D. (2008). Effects of Classroom Instructional Strategies and Self-Beliefs on Science Achievement of Elementary-School Students in Japan: Results from the TIMSS 2003 Assessment. Education, 129(2), 259-266.
  • Istrate, O., Noveanu, G., & Smith, T. M. (2006). Exploring sources of variation in Romanian science achievement. School Quality and Equity in Central and Eastern Europe, 36 (4), 475–496.
  • Johnson, M A., & Lawson, A. (1998). What are the relative effects of reasoning ability and prior knowledge on biology achievement in expository and inquiry classes? Journal of Research in Science Teaching, 35(1), 89-1.
  • Jones, K. K. & Byrnes, J. P. (2006). Characteristics of students who beneit from high-quality mathematics instruction. Contemporary Educational Psychology, 31, 328–343.
  • Kaya, S., & Rice, D. (2010). Multilevel effects of student and classroom factors on elementary science achievement in five countries.International Journal of Science Education , 32 (10), 1337-1363
  • Li, M., Ruiz-Primo, M. A., & Shavelson, R. J. (2006). Towards a science achievement framework: The case of TIMSS 1999. In S. Howie & T. Plomp (Eds.), Contexts of learning mathematics and science: Lessons learned from TIMSS. London: Routledge.
  • Lieberman, A. & McLaughlin, M. (1992). Networks for educational change: Powerful and problematic. Phi Delta Kappan, 73, 673-677.
  • Lomos, C., Roelande, H. H., & Bosker, R. J. (2011). Professional communities and student achievement–A meta-analysis. School Effectiveness and School Improvement, 22(2), 121–148.
  • Louis, K. S., & Marks, H. (1998). Does professional community affect the classroom? Teachers’ work and student work experiences in restructuring schools. American Journal of Education, 106, 532– 575.
  • Martin, M. O., & Mullis, I. V. S. (Eds.). (2012). Methods and procedures in TIMSS and PIRLS 2011. Retrieved from html http://timss.bc.edu/methods/index.html
  • Martin, M.O., Mullis, I. V. S., Foy, P., & Stanco, G. M.. (2012). TIMSS 2011 international results in science. Chestnut Hill, MA: Boston College. http://timss.bc.edu/timss2011/downloads/T11_IR_Science_FullBook.pdf
  • McLaughlin, M. W., & Talbert, J. E. (2001). Professional communities and the work of high school teaching. Chicago, IL: University of Chicago Press.
  • McLaughlin, M., and D.J. McGrath, M.A. Burian-Fitzgerald, L. Lanahan, M. Scotchmer, C. Enyeart, L. Salganik (2005). Student Content Engagement as a Construct for the Measurement of Effective Classroom Instruction and Teacher Knowledge. Washington, D.C.: American Institutes for Research,
  • MEB (2005). İlköğretim fen ve teknoloji dersi (4-5. sınıflar) öğretim programı. Ankara: Devlet Kitapları Müdürlüğü Basımevi.
  • Minner, D. D., Levy, A. J., & Century, J. (2009). 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.
  • Mo, Y., Singh, K., & Chang, M. (2013). Opportunity to learn and student engagement: A HLM study on eighth grade science achievement. Educational Research for Policy and Practice, 12, 3–19.
  • Morales, J. F.D., & Escribano, C. (2013). Predicting school achievement: The role of inductive reasoning, sleep length and morningness–eveningness. Personality and Individual Differences, 55, 106 – 111.
  • Mullis, I.V.S., Martin, M.O., Smith, T.A., Garden, R.A., Gregory, K.D., Gonzalez, E.J., Chrostowski, S.J., & O'Connor, K.M. (2003). TIMSS 2003 assessment framework and specifications. Chestnut Hill, MA: Boston College.
  • Munck, M. (2007). Science Pedagogy, teacher attitudes, and student success. Journal of Elementary Science Education, 19 (2), 13-24.
  • Newman, F. M., Wehlage, G. G., & Lamborn, S. D. (1992). The significance and sources of student engagement. In F. M. Newman (Ed.), (1992). Student engagement and achievement in American secondary schools. New York: Teachers College Press.
  • Osborne, J. F. & Collins, S. (2000). Pupils’ and parents’ views of the school science curriculum. London: King’s College London.
  • Osborne, J. F., Simon, S. & Collins, S.(2003). Attitudes towards science: A review of literature and its implications. International Journal of Science Education, 25(9), 1049-1079.
  • Osborne, J. W. (2000). Advantages of hierarchical linear modeling. Practical Assessment, Research, and Evaluation, 7(1), 1-3.
  • Papanastasiou, E., & Zembylas, M. (2004). Differential effects of science attitudes and science achievement in Australia, Cyprus, and the USA. International Journal of Science Education, 26, 259–280.
  • Powell, C. A., Walker, S. P., Chang, S. M., & Grantham – McGregor, S. M. (1998). Nutrition and education: A randomized trial of the effects of breakfast in rural primary school children. American Journal of Clinical Nutrition, 68, 873-879.
  • Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage.
  • Saed, S., & Hammouri, H. (2010). Does subject matter matter? Estimating the impact of instructional practices and resources on student achievement in science and mathematics: Findings from TIMSS 2007. Evaluation & Research in Education, 23(4), 287-299.
  • Schnabel, K. U., Alfeld, C., Eccles, J. S., Köller, O., & Baumert, J. (2002). Parental influence on students’ educational choices in the United States and Germany: Different ramifications—same effect?. Journal of Vocational Behavior, 60, 178–198.
  • Simpson, R. D., & Oliver, J. S. (1990). A summary of the major influences on attitude toward and achievement in science among adolescent students. Science Education, 74, 1–18.
  • Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453.
  • Srnith, A. & Wohlstetter, P.(2001). Reform through school networks: A new kind of authority and accountability. Educational Policy, 15, 499-519.
  • Stemler, S.E. (2001). Examining school effectiveness at the fourth grade: A hierarchical analysis of the third international mathematics and science study (TIMSS) (Unpublished doctoral dissertation). Boston College, Boston, MA.
  • Utley, B. L., Basile, C. G., & Rhodes, L. K. (2003). Walking in two worlds: master teachers serving as site coordinators in partner schools. Teaching and Teacher Education, 19, 515-528.
  • Von Secker, C. (2002). Effects of inquiry-based teacher practices on science excellence and equity. Journal of Educational Research, 95(3), 151–160.
  • Weinburgh, M. (1995). Gender differences in student attitudes toward science: a meta-analysis of the literature from 1970 to 1991. Journal of Research in Science Teaching, 32, 387–398.
  • Woltman, H., Feldstain, A., MacKay, J. C. & Rocci, M. (2012). An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology, 8(1) 52-69.
  • Young, D.J., Reynolds, A.J., & Walberg, H.J. (1996). Science achievement and educational productivity: A hierarchical linear model. The Journal of Educational Research, 89(5), 272-278.