Research Article
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Implications of between-school tracking for Turkish students

Year 2019, Volume: 8 Issue: 3, 196 - 216, 31.07.2019
https://doi.org/10.19128/turje.453383

Abstract

Previous multilevel analyses for Turkey show that performance
differences of students vary more between schools than within schools. These
school-disparities might be associated with Turkey’s tracking system and
related differences in student body and learning environments of school tracks.
Since it is not known how Turkey’s low-performing vocational, low-performing
academic, and high-performing academic school tracks differ regarding students’
family background, motivational and behavioral engagement of students, and
schools’ learning environments, we analyzed the PISA 2012 data to examine these
differences. Results indicate that Turkish students which attend
high-performing academic schools are more likely to have higher socio-economic
status, display higher confidence in their math ability, are less engaged
during class and are exposed to a richer learning environment than students
attending low-performing academic schools. Policy implications of each finding
are discussed in detail.

References

  • Agresti, A. (2007). An Introduction to categorical data analysis (2nd ed.). New Jersey: Wiley.
  • Alacaci, C., & Erbas, A.K. (2010). Unpacking the inequality among Turkish schools: Findings from PISA 2006. International Journal of Educational Development, 30, 182-192.
  • Becker, B. (2010). Bildungsaspirationen von Migranten: Determinanten und Umsetzung in Bildungsergebnisse [Educational aspirations of immigrants: Determinants and application on educational outcomes]. MZES Workingpapers, 137.
  • Box, G., & Tidwell, P. (1962). Transformation of the independent variables. Technometrics, 4, 531-550.
  • Caner, A., & Okten, C. (2013). Higher education in Turkey: Subsidizing the rich or the poor? Economics of Education Review, 35, 75-92.
  • Dincer, M.A., & Uysal, G. (2010). The determinants of student achievement in Turkey. International Journal of Educational Development, 30, 592–598.
  • Eccles, J.S., & Roeser, R.W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21, 225-241.
  • Eccles, J.S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132.
  • Gamoran, A. (2004). Classroom organization and instructional quality. In M. Wang & J. Walberg (Eds.) Can unlike students learn together? Grade retention, tracking, and grouping (pp. 141-155). Greenwich:Information Age.
  • Giersch, J. (2016). Academic tracking, high-stakes tests, and preparing students for college: How inequality persists within schools. Educational Policy, 1-29.
  • Graham, J.W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576.
  • Groos, T. (2016). Gleich und gleich gesellt sich gern, zu den sozialen Folgen freier Grundschulwahl [Birds of feather flock together, social implications of free primary school choice]. (Workingpaper No. 5). Gütersloh:Bertelsman Stiftung.
  • Guo, J., Parker, P.D., Marsh, H.W., & Morin, A.J. (2015). Achievement, motivation, and educational choices: A longitudinal study on expectancy and value using a multiplicative perspective. Developmental Psychology, 51, 1163-1176.
  • Kleine L.(2014). Der Übergang in die Sekundarstufe I. Die Bedeutung sozialer Beziehungen für den Schulerfolg and die Formation elterlicher Bildungsentscheidungen [Transition into secondary school. Importance of social relations and the development of parental educational decisions]. Bamberg:University of Bamberg Press.
  • Maaz, K., Trautwein, U., Lüdtke, O., & Baumert, J. (2008). Educational transitions and differential learning environments: How explicit between-school tracking contributes to social inequality in educational outcomes. Child Development Perspectives, 2, 99-106.
  • Mann, A., Legewie, J., & DiPrete, T.A. (2015). The role of school performance in narrowing gender gaps in the formation of STEM aspirations: A cross-national study. Frontier in Psychology, 6, 1-11.
  • Marsh, H.W., & Hau, K.T. (2003). Big-Fish--Little-Pond effect on academic self-concept: A cross-cultural (26-country) test of the negative effects of academically selective schools. American Psychologist, 58, 364-376.
  • McNeish, D., & Stapleton, L. (2016). The effect of small sample size on tow level model estimates: A review and illustration. Educational Psychology Review, 28, 295-314.
  • Muijs, D., Harris, A., Chapman, C., Stoll, L., & Russ, J. (2004). Improving schools in socioeconomically disadvantaged areas – a review of research evidence. School Effectiveness and School Improvement, 15, 149-175.
  • Murphy, J. (2010). The educator’s handbook for understanding and closing achievement gaps. Corwin:Thousand Oaks.
  • Muthen, B.O., & Muthen, L.K. (1998-2012). Mplus user’s guide. Los Angeles, CA:Muthen and Muthen.
  • OECD. (2009). PISA data analysis manual. SPSS second edition. Paris: OECD Publishing.
  • OECD. (2012). Equity and quality in education: Supporting disadvantaged students and schools. Paris: OECD Publishing.
  • OECD. (2013). PISA 2012 results: What makes schools successful? Resources, policies and practices (Volume IV). Paris: OECD Publishing.
  • OECD. (2014). PISA 2012 technical report. Paris: OECD Publishing.
  • 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, 193-217.
  • Özel, E., Özel, S., & Thompson, B. (2013). SES-related mathematics achievement gap in Turkey compared to European Union countries. Education and Science, 38, 179-193.
  • Özel, M., Caglak, S., & Erdogan, M. (2013). Are affective factors a good predictor of science achievement? Examining the role of affective factors based on PISA 2006. Learning and Individual Differences, 24, 73–82.
  • Roeser, R.W., Urdan, T.C. & Stephens, J.M. (2009). School as a context of motivation and development. In K.R. Wentzel & A. Wigfield (Eds.) Handbook of Motivation at School (pp. 381-410). New York: Routledge.
  • Satorra, A., & Bentler, P. M. (1999). A scaled difference chi-square test statistic for moment structure analysis (Working paper No. 412). Barcelona: Universitat Pompeu Fabra, Department of Economics.
  • 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.
  • Senler, B., & Sungur, S. (2009). Parental influences on students' self-concept, task value beliefs, and achievement in science. The Spanish Journal of Psychology, 12, 106-117.
  • Simpkins, S.D., Fredericks, J., & Eccles, J.S. (2015). The role of parents in the ontogeny of achievement-related motivation and behavioral choices [Monograph]. Monographs of the Society for Research in Child Development, 317.
  • Stapleton, L., M., McNeish, D., M., & Seung Yang, J. (2016). Multilevel and single-level models for measured and latent variables when data are clustered. Educational Psychologist, 53, 317–330.
  • Tabachnick, B.G., & Fidell, L.S. (2007). Using multivariate statistics, 5th ed. Boston: Pearson.
  • Thomas, D.E., Hierman, K.L., Thompson, C., & Powers, C.J. (2008). Double jeopardy: Child and school characteristics that predict aggressive-disruptive behavior in first grade. School Psychology Review, 37, 516-532.
  • Weininger, E., & Lareau, A. (2003). Translating Bourdieu into American context: The question of social class and family-school relations. Poetics, 31, 375–402.
  • Windle, J. (2014). The rise of school choice in education funding reform: An analysis of two policy moments. Educational Policy, 28, 306-324.
  • Yavuz, M. (2009). Factors that affect mathematics-science (MS) scores in the secondary education institutional exam: An application of structural equation modeling. Educational Sciences: Theory & Practice, 9, 1557-1572.
  • Yildirim, S. (2012). Teacher support, motivation, learning strategy use, and achievement: A multilevel mediation model. The Journal of Experimental Education, 80, 150-172.

Türk öğrenciler için okullar arası izleme uygulamaları

Year 2019, Volume: 8 Issue: 3, 196 - 216, 31.07.2019
https://doi.org/10.19128/turje.453383

Abstract

Çok düzeyli analizler Türkiye’deki okullar arası
öğrenci performansı farklılıklarının okul içi performans farklılıklarından daha
fazla olduğunu göstermiştir. Bu durum, okullara giriş sistemi ve buna bağlı
olarak öğrenci profillerindeki ve de okulların öğrenme ortamlarındaki
farklılıklardan kaynaklanabilmektedir. Türkiye’deki düşük performanslı meslek
okullarına, düşük performanslı akademik okullara ve yüksek performanslı
akademik okullara devam eden öğrencilerin aile geçmişleri, motivasyonel ve
davranışsal katılımları ve okulların öğrenme ortamları arasındaki farklar
yeteri kadar incelenmemiş olduğundan, bu çalışmada PISA 2012 verisi bu
farklılıkları tespit etme amacı ile analiz edilmiştir. Sonuçlar, düşük
performanslı akademik okullara giden öğrencilere kıyasla, yüksek performanslı
akademik okullardaki Türk öğrencilerinin daha yüksek sosyo-ekonomik statüye
sahip olduklarını, matematik becerilerine daha çok güvendiklerini, ders
sırasında daha az katılım gösterdiklerini ve daha zengin bir öğrenme ortamına
maruz kaldıklarını göstermiştir. Bulgular eğitim politikaları kapsamında
tartışılmıştır.

References

  • Agresti, A. (2007). An Introduction to categorical data analysis (2nd ed.). New Jersey: Wiley.
  • Alacaci, C., & Erbas, A.K. (2010). Unpacking the inequality among Turkish schools: Findings from PISA 2006. International Journal of Educational Development, 30, 182-192.
  • Becker, B. (2010). Bildungsaspirationen von Migranten: Determinanten und Umsetzung in Bildungsergebnisse [Educational aspirations of immigrants: Determinants and application on educational outcomes]. MZES Workingpapers, 137.
  • Box, G., & Tidwell, P. (1962). Transformation of the independent variables. Technometrics, 4, 531-550.
  • Caner, A., & Okten, C. (2013). Higher education in Turkey: Subsidizing the rich or the poor? Economics of Education Review, 35, 75-92.
  • Dincer, M.A., & Uysal, G. (2010). The determinants of student achievement in Turkey. International Journal of Educational Development, 30, 592–598.
  • Eccles, J.S., & Roeser, R.W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21, 225-241.
  • Eccles, J.S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132.
  • Gamoran, A. (2004). Classroom organization and instructional quality. In M. Wang & J. Walberg (Eds.) Can unlike students learn together? Grade retention, tracking, and grouping (pp. 141-155). Greenwich:Information Age.
  • Giersch, J. (2016). Academic tracking, high-stakes tests, and preparing students for college: How inequality persists within schools. Educational Policy, 1-29.
  • Graham, J.W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576.
  • Groos, T. (2016). Gleich und gleich gesellt sich gern, zu den sozialen Folgen freier Grundschulwahl [Birds of feather flock together, social implications of free primary school choice]. (Workingpaper No. 5). Gütersloh:Bertelsman Stiftung.
  • Guo, J., Parker, P.D., Marsh, H.W., & Morin, A.J. (2015). Achievement, motivation, and educational choices: A longitudinal study on expectancy and value using a multiplicative perspective. Developmental Psychology, 51, 1163-1176.
  • Kleine L.(2014). Der Übergang in die Sekundarstufe I. Die Bedeutung sozialer Beziehungen für den Schulerfolg and die Formation elterlicher Bildungsentscheidungen [Transition into secondary school. Importance of social relations and the development of parental educational decisions]. Bamberg:University of Bamberg Press.
  • Maaz, K., Trautwein, U., Lüdtke, O., & Baumert, J. (2008). Educational transitions and differential learning environments: How explicit between-school tracking contributes to social inequality in educational outcomes. Child Development Perspectives, 2, 99-106.
  • Mann, A., Legewie, J., & DiPrete, T.A. (2015). The role of school performance in narrowing gender gaps in the formation of STEM aspirations: A cross-national study. Frontier in Psychology, 6, 1-11.
  • Marsh, H.W., & Hau, K.T. (2003). Big-Fish--Little-Pond effect on academic self-concept: A cross-cultural (26-country) test of the negative effects of academically selective schools. American Psychologist, 58, 364-376.
  • McNeish, D., & Stapleton, L. (2016). The effect of small sample size on tow level model estimates: A review and illustration. Educational Psychology Review, 28, 295-314.
  • Muijs, D., Harris, A., Chapman, C., Stoll, L., & Russ, J. (2004). Improving schools in socioeconomically disadvantaged areas – a review of research evidence. School Effectiveness and School Improvement, 15, 149-175.
  • Murphy, J. (2010). The educator’s handbook for understanding and closing achievement gaps. Corwin:Thousand Oaks.
  • Muthen, B.O., & Muthen, L.K. (1998-2012). Mplus user’s guide. Los Angeles, CA:Muthen and Muthen.
  • OECD. (2009). PISA data analysis manual. SPSS second edition. Paris: OECD Publishing.
  • OECD. (2012). Equity and quality in education: Supporting disadvantaged students and schools. Paris: OECD Publishing.
  • OECD. (2013). PISA 2012 results: What makes schools successful? Resources, policies and practices (Volume IV). Paris: OECD Publishing.
  • OECD. (2014). PISA 2012 technical report. Paris: OECD Publishing.
  • 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, 193-217.
  • Özel, E., Özel, S., & Thompson, B. (2013). SES-related mathematics achievement gap in Turkey compared to European Union countries. Education and Science, 38, 179-193.
  • Özel, M., Caglak, S., & Erdogan, M. (2013). Are affective factors a good predictor of science achievement? Examining the role of affective factors based on PISA 2006. Learning and Individual Differences, 24, 73–82.
  • Roeser, R.W., Urdan, T.C. & Stephens, J.M. (2009). School as a context of motivation and development. In K.R. Wentzel & A. Wigfield (Eds.) Handbook of Motivation at School (pp. 381-410). New York: Routledge.
  • Satorra, A., & Bentler, P. M. (1999). A scaled difference chi-square test statistic for moment structure analysis (Working paper No. 412). Barcelona: Universitat Pompeu Fabra, Department of Economics.
  • 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.
  • Senler, B., & Sungur, S. (2009). Parental influences on students' self-concept, task value beliefs, and achievement in science. The Spanish Journal of Psychology, 12, 106-117.
  • Simpkins, S.D., Fredericks, J., & Eccles, J.S. (2015). The role of parents in the ontogeny of achievement-related motivation and behavioral choices [Monograph]. Monographs of the Society for Research in Child Development, 317.
  • Stapleton, L., M., McNeish, D., M., & Seung Yang, J. (2016). Multilevel and single-level models for measured and latent variables when data are clustered. Educational Psychologist, 53, 317–330.
  • Tabachnick, B.G., & Fidell, L.S. (2007). Using multivariate statistics, 5th ed. Boston: Pearson.
  • Thomas, D.E., Hierman, K.L., Thompson, C., & Powers, C.J. (2008). Double jeopardy: Child and school characteristics that predict aggressive-disruptive behavior in first grade. School Psychology Review, 37, 516-532.
  • Weininger, E., & Lareau, A. (2003). Translating Bourdieu into American context: The question of social class and family-school relations. Poetics, 31, 375–402.
  • Windle, J. (2014). The rise of school choice in education funding reform: An analysis of two policy moments. Educational Policy, 28, 306-324.
  • Yavuz, M. (2009). Factors that affect mathematics-science (MS) scores in the secondary education institutional exam: An application of structural equation modeling. Educational Sciences: Theory & Practice, 9, 1557-1572.
  • Yildirim, S. (2012). Teacher support, motivation, learning strategy use, and achievement: A multilevel mediation model. The Journal of Experimental Education, 80, 150-172.
There are 40 citations in total.

Details

Primary Language English
Subjects Other Fields of Education
Journal Section Research Articles
Authors

Wenke Niehues 0000-0002-2443-8846

Yasemin Kisbu-sakarya This is me 0000-0001-8477-3016

Bilge Selçuk This is me 0000-0001-9992-5174

Publication Date July 31, 2019
Acceptance Date July 13, 2019
Published in Issue Year 2019 Volume: 8 Issue: 3

Cite

APA Niehues, W., Kisbu-sakarya, Y., & Selçuk, B. (2019). Implications of between-school tracking for Turkish students. Turkish Journal of Education, 8(3), 196-216. https://doi.org/10.19128/turje.453383

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