Factors affecting the academic achievement in socioeconomically disadvantaged students
The aim of this research is to assess factors affecting achievement of students coming from low socioeconomic background in PISA 2012 and with high and low achievement in mathematics performance. The research population consists of students who were 15 years old as of the date of PISA 2012 assessment. In the Turkey sample, there are 4848 students from a total of 170 schools from 57 cities in 12 statistical region units in PISA 2012. In this research, students within the lowest 33.00% section according to the economic sociocultural status index in the Turkey sample were included. The research was carried out with 218 students showing low achievement in mathematics and 173 students showing high achievement in mathematics including them all in socioeconomically disadvantaged group. As a result of the structural equation model applied considering students’ affective traits and achievement in mathematics, it is observed that the variable “attitude towards school” is a positive and significant predictor in the low achievement group. It is observed that the variable “affective characteristics towards mathematics” is a positive and significant predictor in the high achievement group while “attitude towards school” is a negative predictor of achievement in mathematics. These results can initiate attempts to review educational investments towards students’ achievement and can lead to fund transfers towards fields that can result in higher increase in achievement.
Acemoğlu, D., & Pischke, S. (2001). Changes in the wage structure, family income, and children’s education. European Economic Review Papers and Proceedings, 45, 890-904.
Adıgüzel, A., & Karadaş, H. (2013) Ortaöğretim öğrencilerinin okula ilişkin tutumlarının devamsızlık ve okul başarıları arasındaki ilişki. Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 10(1), 49-66
Alva, S. A. (1991). Academic invulnerability among Mexican-American students: The importance of protective resources and appraisals. Hispanic Journal of Behavioral Sciences, 13(1), 18-34.
Anderson, J. C., & Gerbing, D.W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173.
Arastaman, G., & Balcı, A. (2013). Lise öğrencilerinin yılmazlık algılarının çeşitli değişkenler açısından incelenmesi. Kuram ve Uygulamada Eğitim Bilimleri, 13(2), 915-928.
Arnold, P. F. (2003). Characteristics of families and schools that foster academic resilience: Insights gained from the national education longitudinal study 1988-1994. Unpublished doctoral thesis, Florida State University, Miami.
Baker, D., Goesling, B., & LeTendre, G. (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(3), 291-312.
Benard, B. (1997). Turning it around for all youth: From risk to resilience. New York: ERIC Clearinghouse on Urban Education.
Bentler, P. M., & Chou, C. (1987). Practical issues in structural modeling. Sociological Methods and Research, 16, 78–117.
Borman, G. D., & Overman, L. T. (2004). Academic resilience in mathematics among poor and minority students. The Elementary School Journal, 104(3), 177-195.
Brackenreed, D. (2010). Resilience and risk. International Education Studies, 3(3), 111-121.
Büyüköztürk, Ş. (2010).Sosyal bilimler için veri analizi el kitabı (12. baskı). Ankara: Pegem Akademi.
Ceylan, E. (2009). PISA 2006 sonuçlarına göre Türkiye’de fen okuryazarlığında düşük ve yüksek performans gösteren okullar arasındaki farklar. Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 4(2), 55-75.
Cheng, S. T., & Chan, A. C. M. (2003). The development of a brief measure of school attitude. Educational and Psychological Measurement, 63(6), 1060-1070.
Cheung, G. W., & Rensvold, R. B. (2000). Assessing extreme and acquiscence response sets in cross-cultural research using structural equations modeling. Journal of Cross-Cultural Psychology, 31(2) 187-212.
Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing MI. Structural Equation Modeling, 9, 235-55.
Chevalier, A., & Lanot, G. (2002). The relative effect of family characteristics and financial situation on educational achievement. Education Economics, 10(2), 165-181.
Collins, M. L., & Lanza, S. T. (2010). Latent class and latent transition analysis. USA: Wiley Series.
Davis-Kean, P. E. (2005). The influence of parent education and family income on child achievement: The indirect role of parental expectation and the home environment. Journal of Family Psychology, 19(2), 294-304.
Demir, İ., Kılıç, S., & Ünal, H. (2010). Effects of students and schools characteristics on mathematics achievement: Findings from PISA 2006. Procedia Social and Behavioral Science, 2, 3099–3103.
Dinçer, M. A., & Oral, I. (2013). Türkiye’de devlet liselerinde akademik dirençlilik profili. İstanbul: ERG yayınları.
Dünya Bankası (2011). Türkiye’de temel eğitimde kalite ve eşitliğin geliştirilmesi: Zorluklar ve seçenekler. (Rapor No. 54131-TR).
ERG. (2014). Türkiye’de eğitim sisteminde eşitlik ve akademik başarı araştırma raporu ve analizi. İstanbul: Eğitim Reformu Girişimi.
Fallon, M. C. (2010). School factors that promote academic resilience in urban Latino high school students. Unpublished doctoral thesis. Loyola University, Chicago
Ferguson, R. F. (1998). Teachers' perceptions and expectations and the black-white test score gap. In C. Jencks & M. Phillips (Eds.), The black-white test score gap (pp. 273-317). Washington, DC: Brookings Institution.
Ford, D. Y., & Ill, J. H. (2008). Perceptions and attitudes of black students toward school, achievement and other educational variables. Child Development, 67(3), 1141-1152.
Foster, T. A. (2013). An exploration of academic resilience among rural students living in poverty. Unpublished doctoral thesis. Piedmont College, Georgia.
Gizir, C., & Aydın, G. (2009), Protective factors contributing to the academic resilience of students living in poverty in Turkey, Professional School Counselling, 13(1), 38-49.
Gonzalez, R., & Padilla, A. M. (1997). The academic resilience of Mexican American high school student. Hispanic Journal of Behavioral Sciences, 19(3), 301-317.
Gujarati, D. N. (2004). Basic econometric. New York: The McGraw-Hill Companies.
Hu, L., & Bentler, P. (1995). Evaluating model fit. In R. Hoyle (Eds). Structural equation modeling: Concepst, issues and application (pp. 76-99). Thousand Oasks: Sage Publications.
Jackson, D. L. (2001). Sample size and number of parameter estimates in maximum likelihood confirmatory factor analysis: A Monte Carlo investigation. Structural Equation Modeling, 8, 205-223
Johnson, B. (2008). Teacher-student relationships which promote resilience at school: Amicro-level analysis of students’ views. British Journal of Guidance & Counselling, 36(4), 385-398.
Johnson, R. M. (2000). Gender differences in mathematics performance. Annual Meeting of the American Educational Research Association, New Orleans, LA, USA.
Joreskog, K., & Sorbom, D. (2001). LISREL 8: User’s reference guide. USA: Scientific Software International.
Karasar, N. (2009). Bilimsel araştırma yöntemi: Kavramlar, ilkeler, teknikler. Ankara: 3A Araştırma Eğitim Danışmanlık Ltd.
Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: The Guilford Press.
Konstantopoulos, S. (2006). Effects of teachers on minority and disadvantaged students’ achievement in the early grades. The Elementary School Journal, 110(1), 91-113.
Köse, R. M. (2007). Aile sosyoekonomik ve demografik özellikleri ile okul ve özel dershanenin liselere giriş sınavına katılan öğrencilerin akademik başarıları üzerine etkileri. Eğitim Bilim Toplum Dergisi, 17(5). 46-77.
Krei, M. S. (1998). Intensifying the barriers: The problem of inequitable teacher allocation in low-income urban schools. Urban Education, 33(1), 71–94.
Krovetz, M. (1999). Fostering resiliency. Thrust for Educational Leadership, 28, 28-31.
Lamb, S., & Fullarton, S. (2002). Classroom and school factors affecting mathematics achievement: A comparative study of Australia and the United States using TIMMS. Australian Journal of Education, 46(2), 154-171.
Langford, H., Loeb, S., & Wyckoff, J. (2002). Teacher sorting and the plight of urban schools: A descriptive analysis. Educational Evaluation and Policy Analysis, 24(1), 37–62.
Lee, J., & Stankov, L. (2013). Higher-order structure of noncognitive constructs and prediction of PISA 2003 mathematics achievement. Learning and Individual Differences, 26, 119–130.
Linnakyla, P., & Malin, A. (2008). Finnish students' school engagement profiles in the light of PISA 2003. Scandinavian Journal of Educational Research, 52(6), 583-602.
Maddox, S. L., & Prinz, R. J. (2003). School bonding in children and adolescents: Conceptualization, assessment, and associated variables. Clinical Child and Family Psychology Review, 6, 31-49.
Malindi, M. J., & Machenjedze, N. (2012). The role of school engagement in strengthening resilience among male street children. South African Journal of Psychology, 42(1), 71-81.
Masten, A. S. (2001). Ordinary magic: Resilience processes in development. American Psychologist, 56(3), 227-238
MEB (2015). PISA 2012 Türkiye ulusal nihai raporu. Ankara: EARGED.
Meredith, W. (1993). Measurement invariance, factor analysis, and factorial invariance. Pyschometrika, 58, 525-543.
Morales, E. E. (2008). The resilient mind: The psychology of academic resilience. The Educational Forum, 72, 152–167.
Muthen, L. K., & Muthen, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 4, 599-620.
Norman, E. (2000). Introduction: The strengths perspective and resiliency enhancement-a natural partnership. In E. Norman (Eds.), Resiliency enhancement (p.1-16). New York: Columbia University Press.
Odden, A., & Picus, L. O. (2000). School finance: A policy perspective. New York: McGraw Hill.
OECD. (2003). Student engagement at school: A sense of belonging and participation, results from PISA 2000. OECD Publishing.
OECD. (2007). PISA 2006 science competencies for tomorrow’s World. OECD Publishing.
OECD. (2011). Against the odds: Disadvantaged students who succeed in school. OECD Publishing.
OECD. (2013). PISA 2012 results: (Volume I). OECD Publishing.
Orfield, G. (2004). Dropouts in America: Confronting the graduation rate crisis. Cambridge MA: Harvard Education Press.
Önder, E. (2012). İlköğretimde öğrenci başarısında okullar arası eşitsizliklerin analizi. Yayımlanmamış doktora tezi, Ankara: Gazi Üniversitesi.
Peker, M., & Mirasyedioğlu, Ş. (2003). Lise 2. sınıf öğrencilerinin matematik dersine yönelik tutumları ve başarıları arasındaki ilişki. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 14, 157-166.
Raykov, T., & Marcoulides, G. A. (2006).A first course in structural equation modeling. Mahwah, NJ: Lawrence Erlbaum.
Steenkamp, E. M., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. The Journal of Consumer Research, 25(1), 78- 90
Stevens, P. J. (2009). Applied multivariate statistiscs fort the social sciences. New York: Routledge Taylor and Francis Group.
Şimşek, Ö. F. (2007). Yapısal eşitlik modellemesine giriş: Temel ilkeler ve lisrel uygulamaları. Ankara: Ekinoks Yayınları.
Şirin, S. (2005). Socio-economic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417-53.
Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. Boston: Allyn and Bacon.
Tanaka, J. S. (1987). How big is big enough?: Sample size and goodness of fit in structural equation models with latent variables. Child Development, 58, 134-146.
Tansel, A. (2002). Determinants of school attainment of boys and girls in Turkey: Individual, household and community factors. Economics of Education Review, 21(5), 455-470.
Tapia, M., & Marsh, G. E. (2000). Effect of gender, achievement in mathematics, and ethnicity on attitudes toward mathematics. Annual Meeting of the Mid-South Educational Research Association. Bowling Green, Kentucky.
Tatar, M. (2006). Okul ve öğretmenin öğrenci başarısı üzerindeki etkisi. Milli Eğitim, 171, 156- 166.
Tiet, Q. Q., & Huizinga, D. (2002). Dimensions of the construct of resilience and adaptation among inner city youth. Journal of Adolescent Research, 17, 260-276.
Toland, J., & Carrigan, D. (2011). Educational psychology and resilience: New concept, new opportunities. School Psychology International, 32(1), 95-106.
Ungar, M., & Liebenberg, L. (2013). Ethnocultural factors, resilience, and school engagement. School Psychology International, 34(5), 514-526.
Vandenberg, R. J., & Lance, C. E. (1998). A summary of the ıssues underlying measurement equivalence and their implications for ınterpreting group differences. Research Methods Forum, 3, 1-10.
Wasonga, T. (2002). Gender effects on perceptions of external assets, development of resilience and academic achievement: Perpetuation theory approach. Gender Issues, 20(4), 43-54.
Waxman, H. C., Padron, Y. N. Shin, J. Y., & Rivera, H. H. (2008). Closing the achievement gap within reading and mathematics classrooms by fostering Hispanic students’ educational resilience. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 2(4), 429-439.
Werner, E., & Smith, R. (1992). Overcoming the odds: High-risk children from birth to adulthood. New York: Cornell University Press.
Weston, R., & Gore, JR, P. A. (2006). A brief guide to structural equation modeling. The Counceling Psychologist. 34(5), 719-751.
Widaman, K. F., & Reise, S. P. (1997). Exploring the measurement invariance of psychological instruments: Applications in substance use domain. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp.281-324). Washington: American Psychological Association
Wu, D. A., Li, Z., & Zumbo, B. D. (2007). Decoding the meaning of factorial invariance and updating the practice of multi-group confirmatory factor analysis: A demonstration with TIMSS data. Practical Assessment, Research & Evaluation, 12(3), 1-26.
Yavuz, H. Ç. (2015). Ekonomik bakımdan dezavantajlı öğrencilerin akademik yılmazlık düzeylerinin bazı koruyucu faktörler açısından incelenmesi. Yayımlanmamış yüksek lisans tezi, Ankara: Ankara Üniversitesi.
Yenilmez, K., & Özabacı, N. Ş. (2003). Yatılı öğretmen okulu öğrencilerinin matematik ile ilgili tutumları ve matematik kaygı düzeyleri arasındaki ilişki üzerine bir araştırma. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 14, 132–146.
Yılmaz-Fındık, L., & Kavak, Y. (2013). Türkiye’deki sosyoekonomik açıdan dezavantajlı öğrencilerin PISA 2009 başarılarının değerlendirilmesi. Kuram ve Uygulamada Eğitim Yönetimi, 19(2), 249-273.
Copyright (c) 2018 Emine Önder, Şeyma Uyar (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.