Equity in the Turkish education system: A multilevel analysis of social background influences on the mathematics performance of 15-year-old students

Abstract

This paper aims to discover the level of equity in the Turkish education system using maths outcomes of 15-year-old students in the Programme for International Student Assessment (PISA) exam. In order to do that, associations between various social background variables and student performance are analysed via multilevel models. Female pupils, students from lower socioeconomic backgrounds and students from eastern regions are all found to be disadvantaged in terms of maths outcomes. The biggest gap between students in Turkey is due to school types. The difference between selective academic schools and other types of schools is more than 100 points, which is equal to the difference between almost four grade years. However, the positive effect of attending selective academic schools is smaller for girls. Further investigations indicate that, in the Turkish education system, school type at secondary level represents the effects of socioeconomic background and educational and physical resources at school in addition to previous achievements of pupils. Results of the analyses show that it is very hard to claim that the education system in Turkey is operating in a way that mitigates existing inequalities in the society. To the contrary, there are indications that the Turkish education system itself reproduces inequalities.

Implications of between-school tracking for Turkish students

“…That is, students coming from more advantaged families are more likely to be successful during the admission process to high-performing uppersecondary schools in Turkey. This finding confirms our expectations and is in line with other studies from Turkey or France indicating the positive association between standardized admission exam results and SES (Caner & Okten, 2013;OECD, 2012;Özdemir, 2016). It is also in line with studies suggesting that SES-related educational differences are likely to increase at transition points in selective school systems such as between-school tracking (Schnabel et al, 2002).…”

“…Moreover, these results highlight variation in parents' education-related beliefs among lower-SES Turkish parents. This is important, since school track attendance is likely to hold important implications for students' future academic development, with students attending vocational schools being less likely to attend college (Caner & Okten, 2013;Özdemir, 2016). Furthermore, these results are important, since they suggest that there is variation and possibly room for modification of education-related parental beliefs of lower-SES parents in Turkey.…”

Estudio multinivel sobre las variables explicativas de los resultados de México en PISA 2015

“…Situación que es particularmente más notoria en los países de Latinoamérica (Breton & Canavire-Bacarreza, 2017;Deutsch, 2012, Woitshach et al, 2017, en donde existe una amplia segregación que incide de manera directa en los resultados académicos de los estudiantes (Krüger, 2017). Al respecto, Rodríguez (2005) señaló que las variables de NSE explican más varianza principalmente en los países en los que existe mayor desigualdad social, y, por el contrario, en países con brechas más angostas de desigualdad, son las variables psicológicas las que toman mayor fuerza (Arikan, 2014;Karakolidis et al, 2016;Ortiz, 2013;Özdemir, 2016). Sin embargo, un nivel socioeconómico alto también puede tener un efecto negativo, Schindler (2007) encontró que, en los estudiantes daneses, un mayor NSE se asociaba a menores puntuaciones en Lectura.…”

“…Además del nivel socioeconómico, existen otras características de índole personal que impactan los resultados en PISA. Entre las más citadas se encuentran el género (Deutsch, 2013;Gamazo, Martínez-Abad, Olmos-Miguelañez & Rodríguez Conde, 2018;Karakolidis et al, 2016;Kasapoglu, 2014;Özdemir, 2016;Stoet & Geary, 2014), el estatus migratorio (Gamazo et al, 2018;Karakolidis et al, 2016), la asistencia a educación preescolar (Karakolidis et al, 2016, Ortiz, 2013, la condición de repetidor (Choi & Calero, 2013;Ehmke, Drechsel & Carstensen, 2008;Gamazo et al, 2018;Giménez & Castro, 2017) el número de veces que ha cambiado de escuela y el historial académico previo (Gamazo et al, 2018).…”

“…Sin embargo, al igual que a nivel de escuela, a nivel de estudiante también se identificaron otras variables que también mostraron un poder explicativo, aunque en menor grado. Así, por ejemplo, se encontró que la variable Género aporta de forma distinta en cada competencia (Gamazo et al, 2018;Karakolidis et al, 2016;Kasapoglu, 2014;Özdemir, 2016;Stoet & Geary, 2014). Para Matemáticas y Ciencias, los estudiantes varones son quienes obtienen las mejores puntuaciones, mientras que en Lectura son las mujeres.…”