Session Information
05 SES 08 A, Metrics and Equity
Paper Session
Contribution
Organization for Economic Co-operation and Development (OECD) Programme for International Student Assessment (PISA), as well as the results from International Association for the Evaluation of Educational Achievement (IEA) studies show that, with variations, students’ achievement are related with socio-economic status (SES). According to OECD PISA, student achievements are more affected by schools SES, than their family’s SES, therefore, differences between schools, i.e. segregation according to the socio-economic status (SES) of students' families, is an important aspect, which greatly affects student achievement as well as the overall quality of education.
Historically quality is associated with industry when an industrially made product had to meet certain quality standards (Scherman & Bosker, 2017). In education the concept of “quality” is more complicated than just meeting the fixed quality standards, as education quality depends on the needs of an always-changing society and processes that are closely related to this change, therefore measuring the quality of education can be quite challenging (Kirsch & Braun, 2020). Quality of education includes a variety of indicators at various levels (Sulis et al., 2020). This study is focusing on education quality by evaluating equity in education.
In the last decades equity in education has been in the spotlight of educational research for many counties as the successful education system, that provides equal educational opportunities to all members of society is the basis of a society with high human capital potential. Equity means, that there is a low association between student achievement and their socioeconomic backgrounds (Sulis et al., 2020; European Commission/EACEA/Eurydice, 2020; Frønes et al., 2020). Equal educational opportunities is the basis of a successful education system. That, in turn, provides a highly qualified workforce, contributing to the country's economic and social well-being (Hanushek & Kimko, 2000).
This study aims to evaluate schools socio-economic segregation and its changes in the previous two decades, using the data obtained from 7 cycles of OECD PISA (2000 – 2018).
Method
In this study, data from eight European Union (EU) countries bordering the Baltic Sea (i.e. Latvia, Estonia, Lithuania, Denmark, Sweden, Finland, Poland, and Germany) was analyzed. Index of economic, social and cultural status (ESCS) was used as measure of SES. In each country, students in the highest 10% of SES of their families (i.e. high SES group) and the lowest 10% of SES of their families (i.e. low SES group) were examined. These two groups accordingly had the highest and the lowest achievements in student tests in each country. For school segregation assessment a various indices can be used – e.g., Dissimilarity Index (DI), Isolation Index, Diversity Index, and Segregation Index. Each of them has a slightly different interpretation, but the inter-correlation between them is relatively high (Martínez-Garrido, Siddiqui & Gorard, 2020). Segregation indices measure the extent to which the actual distribution of a group of students across schools differs from the random distribution of the same group of students across different schools. The most common measure of segregation, DI, was used in this study. DI examines two student groups and compares their proportions. Current study examines two different cases – (1) one group allocates students from low SES families and the other group allocates all other students; (2) one group allocates students from high SES families and the other group - all other students. In both cases, the calculation procedures remain the same with a difference in data. In each country 10% groups with the highest and lowest students family SES levels were used. Students from these two groups accordingly have the highest and the lowest achievement levels in student tests (Geske et al., 2015). If the number of one SES group in each school is proportional to the number of this group SES students in the country, then the dissimilarity index will be D = 0. The index will be at its peak if this SES group only attend schools with no other students.
Expected Outcomes
The main research findings for this study were: (1) The lowest school segregation can be observed in Finland and Sweden, however, later on a slight increase in segregation can be observed in Sweden. This could be explained by the school reforms and the introduction of education voucher system that started in the 1990s. (2) The highest segregation indices are in Germany, which, in turn, can be explained by the early division of students according to their achievement. (3) Segregation of schools in Latvia can be rated as average, possibly with a slight tendency to increase. (4) The data analysis show no significant decrease in segregation in the previous two decades, which would promote equal educational opportunities. This coincides with a study carried out in Great Britain from 2000 till 2015. (5) In large schools, with comparison to the low SES group, segregation is significantly lower for the high SES group. (6) There is a relatively high segregation of schools in the high SES group in villages (i.e. in the countryside) in Latvia, Lithuania, Poland and Estonia. This can be explained by the differences in socio-economic distribution between rural and urban areas - in rural areas there are fewer students with high SES, in urban areas - with low SES. The causes of school segregation might be explained as – (1) high SES students' reluctance to (or parents' preference not to) attend small rural schools, (2) exclusion (e.g. through entrance exams) of low SES students from some schools in large cities.
References
European Commission/EACEA/Eurydice (2020). Equity in school education in Europe: Structures, policies and student performance. Eurydice report. Luxembourg: Publications Office of the European Union. (pp.334) Geske, A., Grīnfelds, A., Kangro, A., Kiseļova, R., Mihno, L. (2015). Quality of Education: International Comparison. Latvia in OECD Programme for International Student Assessment. Edited by Andris Kangro. Riga: University of Latvia. Hanushek, E. A., & Kimko, D. D. (2000). Schooling, labor-force quality, and the growth of nations. American Economic Review, 90(5), 1184-1208. https://doi.org/10.1257/aer.90.5.1184 Kirsch, I., & Braun, H. (2020). Changing times, changing needs: enhancing the utility of international large-scale assessments. Large-scale Assess Educ 8, 10 https://doi.org/10.1186/s40536-020-00088-9 Martínez-Garrido, C., Siddiqui, N., Gorard, S. (2020). Longitudinal Study of Socioeconomic Segregation Between Schools in the UK. Estudio Longitudinal de la Segregación Escolar por Nivel Socioeconómico en Reino Unido. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 18(4), 123-141. https://doi.org/10.15366/reice2020.18.4.005 Scherman, V., Bosker, R. J., & Howie, S. J. (2017). Monitoring the Quality of Education in Schools, Examples of Feedback into Systems from Developed and Emerging Economies. Rotterdam: Sense Publishers. ISBN 9789463004534 Sulis, I., Giambona, F., & Porcu, M. (2020). Adjusted indicators of quality and equity for monitoring the education systems over time. Insights on EU15 countries from PISA surveys. Socio-Economic Planning Sciences, 69, 100714. https://doi.org/10.1016/j.seps.2019.05.005
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