Akademik Başarının Yordayıcıları: Sosyoekonomik Düzey ve Okul Türü

Öz

Bu çalışmada sosyoekonomik düzey ve okul türünün akademik başarı üzerindeki etkisi Türkiye’de 10 yıldan uzun bir zaman aralığında uygulanan üç ulusal geçiş sistemi ve iki milyon öğrenciye ait verilerle değerlendirildi. Her bir geçiş sisteminin değerlendirilmesi, kendi kapsamındaki ulusal sınava katılan öğrencilere ait verilerle gerçekleştirildi. Öğrencilerin sosyoekonomik düzeylerinin etkisini kontrol ederek devlet okulları ve özel okullarda eğitim alan öğrencilerin puan ortalamalarını karşılaştırmak için kovaryans analizi kullanıldı. Sosyoekonomik açıdan daha avantajlı okul türü olan özel okullardaki öğrencilerin her üç geçiş sisteminde de dil, matematik ve fen testlerinde diğer öğrencilerden anlamlı ölçüde daha yüksek performans gösterdiği bulgusuna ulaşıldı. Tüm öğrencilerin liselere yalnızca ulusal sınav sonuçlarına göre yerleştirildiği ulusal geçiş sisteminde bu farkın daha da arttığı gözlemlendi. Ayrıca, tüm öğrencilerin liselere sınav puanlarına göre yerleştirildiği geçiş sisteminde sosyoekonomik düzeyin öğrencilerin puanları üzerindeki olumsuz etkisinin de en yüksek düzeye ulaştığı belirlendi. Ulusal geçiş sistemlerinin her üçünde de sosyoekonomik düzeyin kontrol edilmesi durumunda özel okul öğrencilerinin puan ortalamalarının anlamlı ölçüde düştüğü gözlemlendi. Çalışma bulguları, öğrencilerin liseye geçişlerinde okul ayrıştırması uygulanmamasının ya da mümkün olduğunca ertelenmesinin daha yararlı olduğunu göstermekte ve ulusal okul ayrıştırma politikaları açısından önemli sonuçlar sağlamaktadır.

Socioeconomic Status and School Type as Predictors of Academic Achievement

Öz

We evaluated the effects of socioeconomic status and school type on academic achievement based on data from two million students over a 10 year period through three national transition systems in Turkey. Each of the three transition systems has its own national examination, and the data includes only students who took these exams. We used covariance analysis to compare the mean scores of public schools and private schools after controlling the effect of students’ socioeconomic levels. We found that students in private schools, who were socioeconomically stronger, had significantly higher academic achievement levels in language, mathematics, and science tests, and this finding was valid across all three transition systems. These effects were further exuberated when all the students were tracked by means of a national exam and placed into different high schools. It was found that the negative impact of one’s socioeconomic level on students’ scores reached its maximum value when all students were placed into high schools by means of a national exam. In all systems, the mean scores of private school students decreased significantly when the socioeconomic level was controlled. Our research has important implications for school tracking policies, specifically indicating that it would be better to omit or at least delay their deployment to post high-school education.

Anahtar Kelimeler

• School tracking,socioeconomic status,academic achievement,school type,transition systems

Kaynakça

1. Abu Bakar, N., Mamat, I., &Mudassir, I. (2017). Influence of parental education on academic performance of secondary school students in Kuala Terengganu. International Journal of Academic Research in Business and Social Sciences, 7(8), 296–304.
2. Al Şensoy, S., & Sağsöz, A. (2015). Relation between pupils academic achievement and pyhsical conditions of classrooms. Journal of Kirsehir Education Faculty, 16(3), 87–104.
3. Alacacı, C., & Erbaş, A. K. (2010). Unpacking the inequality among Turkish schools: Findings from PISA 2006. International Journal of Educational Development, 30(2), 182–192.
4. Aşıcı, M., Baysal, N., & Şahenk-Erkan, S. (2012). A comparison of the reading comprehension questions in PISA 2009 and national level determination exam in Turkey. Journal of Research in Education and Teaching, 1(2), 210–217.
5. Atılgan, H. (2018). Transition among education levels in Turkey: Past-present and a recommended model. Ege Journal of Education, 19(1), 1–18.
6. Aykaç, N., & Atar, E. (2014).Geçmişten günümüze ilköğretimden ortaöğretime geçiş sisteminin değerlendirilmesi. In A. Akdoğan Bulut İnsan ve A. Yavuz Akengin (eds.). Cumhuriyet'in Kuruluşundan Günümüze Eğitimde Kademeler Arası Geçiş ve Yeni Modeller Uluslararası Kongresi (p. 83–104). Antalya: Atatürk Araştırma Merkezi.
7. Barry, J. (2006). The effect of socio-economic status on academic achievement.(Unpublished master dissertation). Wichita State University, USA.
8. Berberoğlu, G., & Kalender, İ. (2005). Investigation of student achievement across years, school types and regions: SSE and PISA analyses. Educational Sciences and Practice, 4(7), 21–35.

9. Bernstein, B. (1977). Class, codes and control, vol. 3: Towards a theory of educational transmissions (2nd edition). London: Routledge & Kegan Paul.
10. Bernstein, B. (2003). Class, codes and control, vol. 1: Theoretical studies towards a sociology of language (1st edition). London: Routledge.
11. Bertocchi, G., & Spagat, M. (2004). The evolution of modern educational systems: Technical vs. general education, distributional conflict, and growth. Journal of Development Economics, 73, 559–582.
12. Bhat, M., Joshi, J., & Wani, I. (2016). Effect of socio-economic status on academic performance of secondary school students. The International Journal of Indian Psychology, 3(4), 2349–2394.
13. Bölükbaş, S. (2018). The role of social policies and tracking in the academic resilience of poor children in Turkey. (Unpublished doctoral dissertation). Ankara Yıldırım Beyazıt University, Ankara. [In Turkish]
14. Bourdieu, P. (1986). The forms of capital. In J. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education (pp. 241–258). https://www.marxists.org/reference/subject/philosophy/works/fr/bourdieu-forms-capital.htm
15. Caro, D. H., McDonald, J. T., & Willms, J. D. (2009). Socio-economic status and academic achievement trajectories from childhood to adolescence. Canadian Journal of Education, 32(3), 558–590.
16. Cengiz, G., Titrek, O., & Akgun, Ö. E. (2007). The effects of the factors related to schools on the preference of students in secondary education. International Journal of Human Sciences, 4(1), 1–22.
17. Ceylan, E. (2009). Differences between low- and high-performing schools in scientific literacy based on PISA 2006 results in Turkey. Van Yüzüncü Yıl University Journal of Education, 4(2), 55–75.
18. Coleman, J., & Hoffer, T. (1987). Public and private schools: The impacts of communities. New York: Basic Books.
19. Coleman, J. S., Hoffer, T., & Kilgore, S. (1982). Cognitive outcomes in public and private schools. Sociology of Education, 55(2-3), 65–76.
20. Çobanoğlu, F., & Badavan, Y. (2017). The key of successful schools: the correlates of school effectiveness. Pamukkale University Journal of Social Sciences Institute, 26, 114–134. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.
21. Çelik, Z., Boz, N., Arkan, Z., & Toklucu, D. K. (2017). TEOG yerleştirme sistemi: Güçlükler ve öneriler. Ankara: Siyaset, Ekonomi ve Toplum Araştırmaları Vakfı.
22. Davies, S., & Guppy, N. (2010). The schooled society: An introduction to the sociology of education (2 edition). Ont: Oxford University Press.
23. Demirtaş, Z. (2010).The relationship in high schools between school culture and student achievement. Mustafa Kemal University Journal of Social Sciences Institute,7(13), 208– 223.
24. ERG (2009). Herkes için kaliteli eğitim. İstanbul: ERG. http://www.egitimreformugirisimi.org/dosyalar/faaliyet/2009.pdf.
25. Ferreira, F. H. G., Gignoux, J., & Aran, M. (2010). Inequality of economic opportunity in Turkey: An assessment using asset indicators and women’s background variables. (Working Paper Number 3.) Ankara: State Planning Organization of the Republic of Turkey and World Bank Welfare and Social Policy Analytical Work Program.
26. Figlio, D.N., & Stone, J.A. (1997). School choice and student performance: Are private schools really better? (Discussion Paper 1141-97.) Institute for Research on Poverty, University of Wisconsin-Madison.
27. Filho, D. B. F., Paranhos, R., da Rocha, E. C., Batista, M., da Silva Jr., J. A., Santos, M. L. W. D., & Marino, J. G. (2013). When is statistical significance not significant? Brazilian Political Science Review, 7(1), 31–55.
28. Goldhaber, D. D. (1996). Public and private high schools: Is school choice and answer to the productivity problem?.Economics of Education Review, 15(3), 93–109.
29. Gooding, Y. (2001). The relationship between parental educational level and academic success of college freshmen.(Unpublished doctoral dissertation). Iowa State University, Iowa.
30. Gümüş, S., & Atalmış, E. H. (2012). Achievement gaps between different school types and regions in Turkey: Have they changed over time? Mevlana International Journal of Education, 2(2), 48–64.
31. Gür, B., Çelik, Z., & Coşkun, İ. (2013). Türkiye’deortaöğretimingeleceği: Hiyerarşi mi eşitlik mi? (Analiz no. 69). Ankara: SETA Vakfı.
32. Gür, B. S., Çelik, Z., Bozgeyikli, H., & Yurdakul, S. (2018). Eğitime bakış 2018: İzleme ve değerlendirme raporu. Ankara: Eğitim-Bir-Sen Stratejik Araştırmalar Merkezi.
33. Hanushek, E.A., & Woessmann, L. (2006). Does educational tracking affect performance and inequality? Differences-in-differences evidence across countries. The Economic Journal 116, 63–76.
34. Hanushek, E.A., Schwerdt, G., Woessman, L., & Zhang, L. (2017). General education, vocational education, and labor-market outcomes over the life-cycle. The Journal of Human Resources, 52(1), 48–87.
35. Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
36. Kaplan, R. M., Chambers, D. A., & Glasgow, R. E. (2014). Big data and large sample size: A cautionary note on the potential for bias. Clinical and Translational Science, 7(4), 342–346.
37. Kuder, G. F., & Richardson, M. W. (1937). The theory of estimation of test reliability. Psychometrika, 2, 151–160.
38. Lazenby, H. (2016). What is equality of opportunity in education?.Theory and Research in Education, 14(1), 65–76.
39. Lubienski, S. T., & Lubienski, C. (2006). School sector and academic achievement: A multi-level analysis of NAEP mathematics data. American Educational Research Journal, 43(4), 651–698.
40. Ma, X., & Klinger, D. A. (2000). Hierarchical linear modelling of student and school effects on academic achievement. Canadian Journal of Education, 25, 41–55. MEB (2016). PISA 2015 ulusal raporu. Ankara: Milli Eğitim Bakanlığı.
41. MEB (2018a). 2018 Liselere Geçiş Sistemi (LGS): Merkezi sınavla yerleşen öğrencilerin performansı. Ankara: Milli Eğitim Bakanlığı.
42. MEB (2018b). Milli Eğitim istatistikleri: Örgün eğitim 2017-2018. Ankara: Milli Eğitim Bakanlığı.
43. Merton, R. K. (1968). The Matthew effect in science. Science, 159, 53–63.
44. Müller, W.,& Shavit, Y. (1998). The institutional embeddedness of the stratification process: A comparative study of qualifications and occupations in thirteen countries. In Shavit, Y., & Müller, W. (eds.) From School to Work: A Comparative Study of Educational Qualifications and Occupational Destinations. Oxford: Clarendon Press, pp. 1–47.
45. Mwiti, M. A. (2012). Influence of school characteristics on students’ academic performance in secondary schools ın Tigania central division, Meru Kenya. A research project report for the award of a Master of Arts degree in project planning and management, University of Nairobi.
46. NCES (2012). Improving the measurement of socioeconomic status for the national assessment of educational progress: A theoretical foundation. The National Center for Education Statistics (NCES) Expert Panel Report. Retrieved from https://nces.ed.gov/nationsreportcard/pdf/researchcenter/Socioeconomic_Factors.pdf
47. Neuman, S., & Ziderman, A. (1991). Vocational schooling, occupational matching, and labor market earnings in Israel. The Journal of Human Resources, 26(2), 256–281.
48. OECD (2004). How student performance varies between schools and the role that socio-economic background plays in this. Paris: OECD Publishing.
49. OECD (2016). PISA 2015 results (Volume I): Excellence and equity in education. Paris: OECD Publishing.
50. Ozer, M. (2018). The 2023 Education Vision and new goals in vocational and technical education. Journal of Higher Education and Science, 8(3), 425–435.
51. Ozer, M. (2019a). Reconsidering the fundamental problems of vocational education and training in Turkey and proposed solutions for restructuring. İstanbul Üniversitesi Sosyoloji Dergisi, 39(2), 1–19.
52. Ozer, M. (2019b). Background of problems in vocational education and training and its road map to solution in Turkey’s Education Vision 2023. Journal of Higher Education and Science, 9(1), 1–11.
53. Ozer, M., & Suna, H. E. (2019). Future of vocational and technical education in Turkey: Solid steps taken after Education Vision 2023. Journal of Education and Humanities, 10(20), 165–192.
54. Ozer, M. (2020). What PISA tells us about performance of education systems. Bartın University Journal of Faculty of Education, 9(2), 217–228.
55. Ozer, M., & Perc, M. (2020). Dreams and realities of school tracking and vocational education. Palgrave Communications, 6, 34.
56. Ozer, M., & Suna, H. E. (2020). The linkage between vocational education and labor market in Turkey: Employability and skill mismatch. Kastamonu Education Journal, 28(2), 558–569.
57. Önder, E., & Güçlü, N. (2014). Solution proposals intended to reduce success difference among primary schools. Journal of Educational Sciences,40, 109–132.
58. ÖSYM (2018). 2018 Yükseköğretim Kurumları Sınavı değerlendirme raporu. Ankara: ÖSYM.
59. Perc, M. (2014). The Matthew effect in empirical data. Journal of The Royal Society Interface 11(98), 1–15.
60. Perry, L., & McConney, A. (2010). School socio-economic composition and student outcomes in Australia: Implications for educational policy. Australian Journal of Education, 54(1), 72–85.
61. Reichelt, M., Collischon, M., & Eberl, A. (2019). School tracking and its role in social reproduction: Reinforcing educational inheritance and the direct effects of social origin. The British Journal of Sociology, 70(4), 1–26.
62. Saracaloğlu, A. S., Yakar, A., & Altay, B. (2014). Türkiye'de ortaokula geçiş sürecinde yaşanan sorunlar ve özel dershaneler: Öğrenci-öğretmen-veli görüşleri (Muğla ve Aydın örnekleri). In A. Akdoğanbulut İnsan, & A. Yavuz Akengin (eds.), Cumhuriyet'in Kuruluşundan Günümüze Eğitimde Kademeler Arası Geçiş ve Yeni Modeller Uluslararası Kongresi (pp. 347–386). Antalya: Atatürk Araştırma Merkezi.
63. Scott, L.A., Rock, D.A., Pollack, J.M., & Ingels, S.J. (1994). Two years later: Cognitive gains and school transitions of NELS:88 eighth graders. U.S. Department of Education-National Center for Education Statistics, Statistical Analysis Report.
64. Schreiber, B., Stage, F.K., King, J., Nora, A., & Barlow, E.A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. Journal of Educational Research, 99(6), 323–337.
65. Singh, K. (2014). Right to education and equality of educational opportunities. Journal of International Cooperation in Education, 16(2), 5–19.
66. Suna, H. E., Tanberkan, H., & Ozer, M. (2020). Changes in literacy of students in Turkey by years and school types: Performance of students in PISA applications. Journal of Measurement and Evaluation in Education and Psychology, 11(1), 76-97.
67. Sutton, A., & Soderstrom, I. (1999). Predicting elementary and secondary school achievement with school-related and demographic factors. Journal of Educational Research, 92(6), 330–338.
68. Sweetland, S. R., & Hoy, W. K. (2000). School characteristics& educational outcomes: Toward an organizational model of student achievement in middle schools. Educational Administration Quarterly, 36(5), 703–729.
69. Şirin, S. R. (2005). Socioeconomic status and academic achievement: A meta‐analytic review of research. Review of Educational Research, 75(3), 417–453.
70. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Pearson.
71. TED (2010). Ortaöğretime ve yükseköğretime geçiş sistemi. Ankara: TED Publishing.
72. Thiele, T., Singleton, A., Pope, D., & Stanistreet, D. (2015). Predicting students’ academic performance based on school and socio-demographic characteristics. Studies in Higher Education, 41(8), 1424–1446.
73. Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: How to use principal components analysis. Health Policy and Planning, 21(6), 459–468.
74. Waters, S., Cooper, S., Kuncel, N., Sackett, P. R., & Arneson, J. (2009). Socioeconomic status and the relationship between the SAT and freshman GPA: An analysis of data from 41 colleges and universities (College Board Research Report Sy 2009-1). New York, NY: College Board.
75. White, K. (1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91, 461–481.
76. Woessmann, L. (2009). International evidence on school tracking: A review. CESifo DICE Report, 26–34.
77. World Bank (2013). Promoting excellence in Turkey’s schools (Sy 77722; ss. 1–45). Washington, DC: The World Bank.
78. Yılmaz Fındık, L., & Kavak, Y. (2013). Assessing the PISA 2009 achievement of disadvantaged students in Turkey. Educational Administration: Theory and Practice,19(2), 249–273.
79. Yudd Moscoso, R. (2000). The effects of school characteristics on student academic performance. (Unpublished doctoral dissertation). Virginia Polytechnic Institute and State University, USA.
80. Zimmer, R. (2003). A new twist in the educational tracking debate. Economics of Education Review, 22(3), 307–315.