The Effects of Student and School Level Characteristics on Academic Achievement of Middle School Students in Turkey

Yıl 2019, Cilt: 10 Sayı: 4, 391 - 405, 13.12.2019

Pınar Karaman Burcu Atar

https://doi.org/10.21031/epod.564819

Cited By: 1

Öz

The purpose
of the study was to examine the student-level and school-level variability that
affect middle school students’ academic achievement. Student background and
school context on student academic achievement were examined. Participants of
the study consisted of 1053 seventh and eighth grade middle school students
from 10 schools in the cities of Ankara and Sinop, Turkey. The research study
analysed using two-level hierarchical linear modeling (HLM). Data were analysed
with three HLM models: (1) random effects one-way ANOVA model, (2) random
coefficients regression model, (3) intercepts and slopes-as outcomes model. The
results of the analyses showed that at the student level, gender, SES, and
number of siblings were found to have statistically significant effects on student
GPA. When considering the practical importance of student level variables, SES,
and number of siblings have small effects, but gender has a moderate effect on
students’ school achievements. On average, female students perform higher than
male students in terms of their GPA scores. At the school level, educational
school resources have a significant effect on predicting academic achievement.
It has been shown that school resources have a moderate effect on students’
academic achievements.

Anahtar Kelimeler

Hierarchical linear modeling, academic achievement, student GPA, gender, SES, school resources

Destekleyen Kurum

Sinop University Scientific Research Project (BAP)

Proje Numarası

EĞTF-1901-18-09

Kaynakça

• Adeogun, A. A., & Osifila, G. I. (2008). Relationship between educational resources and students’ academic performance in Lagos State Nigeria. International Journal of Educational Management, 5-6, 144-153.
• Akyüz, G. (2014). TIMSS 2011’de öğrenci ve okul faktörlerinin matematik başarısına etkisi. Eğitim ve Bilim, 39(172), 150-162.
• Akyuz, G., Berberoglu, G. (2010). Teacher and classroom characteristics and their relations to mathematics achievement of the students in the TIMSS. New Horizons in Education, 58(1): 77-95.
• Alacaci, C., & Erbaş, A. K. (2010). Unpacking the inequality among Turkish schools: Findings from PISA 2009. International Journal of Educational Development, 30(2), 182-192.
• Anıl, D. (2009). Uluslararası öğrenci başarılarını değerlendirme programı (PISA)’nda Türkiye’deki öğrencilerin fen bilimleri başarılarını etkileyen faktörler. Eğitim ve Bilim, 34, 87-100.
• Aypay, A., Erdogan, M., Sozer, M.A., (2007). Variation among schools on classroompractices in science based on TIMSS-1999 in Turkey. Journal of Research in Science Teaching 44 (10), 1417–1435.
• Atar, H. Y. (2014). Öğretmen niteliklerinin TIMSS 2011 fen başarısına çok düzeyli etkileri. Eğitim ve Bilim, 39(172), 121-137.
• Atar, H. Y., & Atar, B. (2012). Examining the effects of Turkish Education reform on students' TIMSS 2007 science achievements. Educational Sciences: Theory and Practice, 12(4), 2632-2636.
• Baker, D. P., Goesling, B., & Letendre, G. K. (2002). Socioeconomic status, school quality, and national economic development: A cross-national analysis of the “Heyneman Loxley Effect” on Mathematics and Science achievement. Comparative Education Review, 46, 291–312.
• Batyra, A. (2017). Gender Gaps In Student Achievement In Turkey: Evidence From The Programme For International Student Assessment (PISA) 2015. Istanbul: Education Reform Initiative.
• Bellibaş, M. Ş. (2016). Who are the most disadvantaged? Factors associated with the achievement of students with low socio-economic backgrounds. Educational Sciences: Theory &Practice, 16, 691-710
• Berberoğlu, G., (2004). Student Learning Achievement. Paper Commissioned for the Turkey ESS. World Bank, Washington, DC
• Bilican-Demir, S. (2018). The effect of teaching quality and teaching practices on PISA 2012 mathematics achievement of turkish students, International Journal of Assessment Tools in Education, 5(4), 645–658.
• Boedeker, Peter (2017). Hierarchical Linear Modeling with Maximum Likelihood, Restricted Maximum Likelihood, and Fully Bayesian Estimation. Practical Assessment, Research & Evaluation, 22(2), 1-19.
• Börkan, B., & Bakış, O. (2016). Determinants of academic achievement of middle schoolers in Turkey. Educational Sciences: Theory & Practice, 16, 2193–2217. Brooks-Gunn, J., & Duncan, G.J., (1997). The effects of poverty on children. The Future of Children, 7 (2), 55–71.
• Buchmann, C., Hannum, E. (2001). Education and stratification in developing countries: a review of theories and research. Annual review of Sociology, 27, 77–103.
• Card, D., Krueger, A.(1996). School resources and student outcomes: an overview of the literature and new evidence from North and South Carolina. Journal of Economic Perspectives 10, 31–40.
• Chiu, M. M. & Xihua, Z. (2008). Family and motivation effects on mathematics achievement: Analyses of students in 41 countries. Learning and Instruction, 18(4), 321–336.
• Coleman, J.S., Campbell, E.Q., Hobson, C.J., McPartlant, J., Mood, A.M., Weinfall, F.D., York, R.L. (966). Equality of Educational Opportunity. Department of Health, Education and Welfare, Washington, DC
• Cohen, J. (1992). A power primer. Psychological Bulletin, 112,155–159.
• Çiğdem-Yavuz, Nükhet-Demirbaşlı, R., & Yalçın, S., & İlgün-Dibek, M. (2017). The effects of student and teacher level variables on TIMSS 2007 and 2011 mathematics achievement of Turkish students. Education and Science, 42 (189), 27-47. Çiftçi, Ş. K. (2015). Effects of secondary school student’ perceptions of mathematics education quality on mathematics anxiety and achievement. Educational Sciences: Theory & Practice, 15(6), 1487–1502.
• Darling-Hammond, L., 2000. Teacher quality and student achievement: a review of the state policy evidence. Education Policy Analysis Archives 8 (1), 1–30.
• Dincer MA and Uysal G (2010). The determinants of student achievement in Turkey. International Journal of Educational Development, 30(6): 592–598.
• Downey, D. B. (2001). Number of siblings and intellectual development. The resource dilution explanation. American Psychologist, 56(6), 497–504.
• Engin-Demir, C., 2009. Factors influencing the academic achievement of the Turkish urban poor. International Journal of Educational Development, 29, 17–29.
• Farkas, G., Sheehan, D., Grobe, R.P., 1990. Coursework mastery and school success: gender, ethnicity, and poverty groups within an urban school district. American Educational research Journal 27 (4), 807–827.
• Flores, A. (2007). Examining disparities in mathematics education: Achievement gap or opportunity gap? The High School Journal, 91(1), 29–42.
• Fuller, B., & Clarke, P. (1994). Raising school effects while ignoring culture? Local conditions and the influence of classroom tools, rules and pedagogy. Review of Educational Research 64, 122–131.
• Gamboa, L. F., & Waltenberg, F. D. (2012). Inequality of opportunity in educational achievement in Latin America: Evidence from PISA 2006–2009. Economics of Education Review, 31(5), 694–708.
• Gelbal, S. (2008). Sekizinci Sınıf Öğrencilerinin Sosyoekonomik Özelliklerinin Türkçe Başarısı Üzerinde Etkisi. Egitim ve Bilim, 33(150), 1–13.
• Gevrek, Z. E., & Seiberlich, R. R. (2014). Semiparametric decomposition of the gender achievement gap: An application for Turkey. Labour Economics, 31, 27–44.
• Güvendir, M. A. (2014). Öğrenci başarılarının belirlenmesi sınavında öğrenci ve okul özelliklerinin Türkçe başarısı ile ilişkisi. Eğitim ve Bilim, 39(172), 163-180. Gill, J. (2003). Hierarchical linear models. In Kimberly Kempf-Leonard (Ed.), Encyclopedia of social measurement. New York: Academic Press.
• Goldstein, H. (2011). Multilevel statistical models (Vol. 922). John Wiley & Sons.
• Hanushek, E.A. (1997). Assessing the effects of school resources on student performance: an update. Educational Evaluation and Policy Analysis, 19 (2), 141–164.
• Hanushek, E.A., Luque, J.A. (2003). Efficiency and equity in schools around the world. Economics of Education Review, 22, 481–502.
• Hox J.J. (1995). Applied Multilevel Analysis. Amsterdam: TT-Publicaties.
• Hox, J. J. (1998). Multilevel modeling: when and why. In I. Balderjahn, R. Mathar, & M. Schader (Eds.), Classification, data analysis, and data highways (pp. 147–154). Berlin: Springer.
• Hox, J. (2010). Multilevel analyses: techniques and applications (2nd ed.). Mahwah, NJ: Erlbaum.
• Kalender, I., & Berberoglu, G. (2009). An assessment of factors related to science achievement of Turkish students. International Journal of Science Education, 31(10), 1379–1394.
• Kelley, K., & Preacher, K. J. (2012). On efect size. Psychological Methods, 17,137–152.
• Kreft, I. G. G. (1996). Are multilevel techniques necessary? An overview, including simulation studies. Unpublished manuscript, California State University, Los Angeles.
• Krueger, A.B. (2003). Economic considerations and class size. The Economic Journal, 113 (485), F34–F63.
• Ma, X., (2001). Stability of socioeconomic gaps in mathematics and science achievement among Canadian schools. Canadian Journal of Education 26 (1), 97–118.
• Maas, C. J. M., & Hox, J. J. (2005). Sufficient sample sizes for multilevel modeling. Methodology, 1(3), 86–92.
• Mancebon, M.J., Mar Molinero, C. (2000). Performance in primary schools. Journal of the Operational Research Society, 51, 843–854.
• Marsh, H. W., Trautwein, U., Lüdtke, O., Köller, O., and Baumert, J. (2005). Academic self-concept, interest, grades, and standardized test scores: reciprocal effects models of causal ordering. Child Dev. 76, 397–416.
• McNeish, D. M., & Stapleton, L. M. (2016). The effect of small sample size on two-level model estimates: A review and illustration. Educational Psychology Review, 28, 295–314.
• National Ministry of Education (MONE), (2007). Report on Student Assessment Program (SAP) 2005: Mathematics. MONE Directorate of Education and Instruction, Ankara.
• Osborne, J. W. (2000). Advantages of hierarchical linear modeling. Practical Assessment, Research, and Evaluation, 7(1), 1-3.
• Özdemir, C. (2016). Equity in the Turkish education system: A multilevel analysis of social background influences on the mathematics performance of 15-year-old students. European Educational Research Journal, 15(2 ),193-217.
• Özberk, E.H., Atalay- Kabasakal, K., Boztunç-Öztürk, N. (2017). Investigating the factors affecting turkish students’ PISA 2012 mathematics achievement using hierarchical linear modeling. Hacettepe University Journal of Education, 32(3), 544-559.
• Parcel, T.L., Dufur, J.M., (2001). Capital at home and at school: effects on student achievement. Social Forces 79 (3), 881–911.
• Perry, L., & 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.
• Petrill, S. A., & Wilkerson, B. (2000). Intelligence and achievement: A behavioral genetic perspective. Educational Psychology Review, 12, 185-199.
• Phan, H. T. (2008). Correlates of Mathematics Achievement in Developed and Developing Countries: An HLM Analysis of TIMSS 2003 Eighth-grade Mathematics Scores. PhD Dissertation, Unpublished. Tampa: University of South Florida.
• Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods, 2nd ed. Thousand Oaks: Sage Publications.
• Raudenbush, S., Bryk, A., Cheong, Y.F., Congdon, R., & du Toit, M. (2011). HLM 7: Hierarchical Linear and Nonlinear Modeling. Lincolnwood, IL: Scientific Software International, Inc.
• Smits, J., & Gündüz-Hoşgör, A. (2006). Effects of family background characteristics on educational participation in Turkey. International Journal of Educational Research, 26, 545–560.
• Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: an introduction to basic and advanced multilevel modeling (2nd ed.). London: Sage.
• Tabachnick, B. G., and Fidell, L. S. (2014). Using Multivariate Statistics, 6th Edn. New York, NY: Pearson Education
• Tavşancıl, E., & Yalçın, S. (2015). A determination of Turkish student’s achievement using hierarchical linear models in trends in ınternational mathematics-science study (TIMSS) 2011. Anthropologist, 22(2), 390-396.
• Tremblay, S., Ross, N., Berhelot, J.M., 2001. Factors affecting grade 3 student performance in Ontario: a multi-level analysis. Education Quarterly Review, 7 (4), 25–36.
• UNICEF (2016). Gender Equality in Secondary Education. A literature review for UNICEF, NATCOM and Aydın Doğan Foundation.
• Van Houtte, M. (2004). Why boys achieve less at school than girls: the difference between boys’ and girls’ academic culture. Educational Studies, 30 (2), 159–173.
• Veenstra, R., Kuyper, H. (2004). Effective students and families: the importance of individual characteristics for achievement in high school. Educational Research and Evaluation, 10 (1), 41–70.
• Wößmann, L. (2003). Schooling resources, educational institutions and student performance: The international evidence. Oxford Bulletin of Economics and Statistics, 65(2), 117–170.
• Willms, J.D., (1996). Indicators of mathematics achievement in Canadian elementary schools. In: HRDC (Eds.), Growing up in Canada: National Longitudinal Study of Children and Youth. Human Resources Development Canada and Statistics Canada, Ottawa, Ontario, pp. 69–82.
• Woltman, H., Feldstain, A., MacKay, J. C., & Rocchi, M. (2012). An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology, 8 (1): 52–69.
• Yalçın, S., Demirtaşlı, R. N., Dibek, M. I., & Yavuz, H. C. (2017). The Effect of Teacher and Student Characteristics on TIMSS 2011 Mathematics Achievement of Fourth-and Eighth-Grade Students in Turkey. International Journal of Progressive Education, 13(3), 79-94.
• Yavuz, E., Tan, Ş., & Atar, H., Y. (2019). Effects of students and school variables on SBS achievements and growth in mathematic. Journal of Measurement and Evaluation in Education and Psychology, 10(1), 96-116.