05. Children and Youth at Risk and Urban Education
Paper
School Socioeconomic Segregation: Results from Seven Cycles of PISA
Andrejs Geske, Rita Kiselova, Olga Pole
University of Latvia, Latvia
Presenting Author: Geske, Andrejs
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).
Methodology, Methods, Research Instruments or Sources UsedIn 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.
Conclusions, Expected Outcomes or FindingsThe 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.
ReferencesEuropean 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
05. Children and Youth at Risk and Urban Education
Paper
Tracking in Context: Variation in the Effects of Reforms in the Age at Tracking on Educational Mobility
Michael Grätz1, Marieke Heers1,2
1University of Lausanne; 2FORS
Presenting Author: Heers, Marieke
Educational systems have effects on the intergenerational transmission of education (Breen and Jonsson 2005; van de Werfhorst and Mijs 2010). In particular, previous research has identified one central institutional factor as influencing the intergenerational transmission of educational advantage: between-school tracking, which increases educational inequalities (e.g., Canaan 2020). The extent to which tracking impacts educational inequalities depends on the age at between-school tracking (Brunello and Checchi 2007; Hanushek and Wößmann 2006; Pfeffer 2008). A later age at tracking in an education system reduces educational inequalities and increases intergenerational educational mobility (van de Werfhorst and Mijs 2010).
The most convincing empirical evidence in favor of causal effects of age at (first) tracking on intergenerational educational mobility comes from research estimating the effects of educational reforms in the age at tracking (e.g., van de Werfhorst 2018, 2019). This literature has implicitly assumed that the effects of such reforms do not vary across contexts. However, there are theoretical reasons to expect heterogeneity in the effects of reforms in the age at tracking on the intergenerational transmission of education by context.
In this study, we develop two expectations of such variation. First, the effects of the reforms may vary with the size of the change in the age at tracking. There may be reforms in the age at tracking, which are too incremental, to increase educational mobility. Contrary to this expectation, previous research has treated all countries which track students at an age younger than 15 as early tracking countries (e.g., Hanushek and Wößmann 2006; Scheeren 2022), and thereby ignored the considerable variation in terms of the size in the change of the age at educational selection within this group. It seems, however, at least theoretically possible that reforms in the age at tracking could increase educational mobility more if they shift the age at tracking by more than by less years.
Second, the effects of reforms in the age at tracking on educational mobility may be stronger in countries with stronger egalitarian values. In a climate of more egalitarian societal values there might be more possibilities for reforms to translate into actual outcomes for students and the intended equalizing effects of the reforms might more easily materialize in positive outcomes for students, especially for those from parents with lower levels of education. This hypothesis has been expressed by earlier research (van de Werfhorst 2018:32) but it has not been empirically tested.
The present study tests whether the effects of reforms in the age at tracking on educational mobility vary across countries. There are two kinds of previous studies on the consequences of tracking, neither of them allows us to answer this question. On the one hand, previous research has investigated the effects of reforms in tracking age in single country studies (e.g., Canaan 2020 on France and Dronkers 1993 on the Netherlands). These studies point towards positive implications of increasing the age at tracking and educational attainment (Canaan 2002; Dronkers 1993; Wielemans 1991). Variation in results across different studies can, however, be based on differences in the research design and the operationalization of variables. Yet, it is plausible that these differences are due to contextual differences. On the other hand, previous comparative research has analyzed multiple countries in difference-in-difference designs and therefore, by design, excluded the possibility of cross-country variation in the effects of reforms on educational mobility (Brunello and Checchi 2007; Hanushek and Wößmann 2006; van de Werfhorst 2018, 2019; Scheeren 2022).
In addition, we analyze gender differences in these effects. The investigation of gender differences is motivated by the inconclusive findings from previous studies.
Methodology, Methods, Research Instruments or Sources UsedWe use data from the European Social Survey (ESS) and the Survey of Health, Ageing and Retirement in Europe (SHARE). We use both data sources to increase the sample sizes. This is needed to improve the precision of our estimates. Whilst the ESS provides a sample which is representative for the adult population in each country, SHARE is representative of the population aged 50 years and older as well as their partners. Both data sets cover the countries with reforms included in our analysis and the cohorts that have been affected by the reforms. We use data from all waves of the survey data sets, which were available at the time of writing. This means we used waves 1 to 10 (2002–2020) from the ESS data and waves 1 to 8 (2004–2017) from SHARE. SHARE is a panel data set. However, on each respondent we only use the information once, and we always use the most recent information to measure educational attainment.
We analyze the effects of reforms in age at tracking on educational mobility using a regression discontinuity design (RDD). We estimate OLS and linear probability regression models (LPMs).
Conclusions, Expected Outcomes or FindingsThere are good theoretical reasons to believe that the effects of the reforms may vary across contexts. Yet, our results are in line with the implicit expectation of prior studies, which assume no cross-country variation in the effects of reforms in the age at tracking on educational mobility than with both our hypotheses. Neither the size of the increase in the age at tracking (hypothesis 1), nor the extent to which there are egalitarian values are prevailing in a society (hypothesis 2) impact the effect of an age increase in tracking on educational mobility. In fact, it is quite astonishing how robust the positive effects of reforms in the age at tracking are for fostering educational mobility. In all five countries, the reforms have clearly improved the educational attainment of children with low educated parents more than for children with medium and highly educated parents. Hence, the reforms have contributed to reducing educational inequalities.
There are heterogeneities in the results across the three outcome variables. With respect to years of education there is an overall reduction in educational inequalities. The results for the other two dependent variables, upper-secondary education completion, and even more so higher education completion reveal fewer impacts of the reforms. This shows that the effects of the reforms are found rather at the lower than at the higher end of educational attainment, which are also the levels of education directly affected by the reforms. Finally, in most countries quite clearly the reforms in age at tracking have only affected the completion of upper secondary but not the completion of post-secondary education. This implies that there are no positive spillover effects of changing the age at tracking for tertiary education. In addition, our results with respect to gender are not in line with our theoretical expectations.
ReferencesAllmendinger, Jutta. 1989. “Educational Systems and Labor Market Outcomes.” European Sociological Review 5:231–50.
Bol, Thijs, and Herman G. van de Werfhorst. 2013. “Educational Systems and the Trade-Off between Labor Market Allocation and Equality of Educational Opportunity.” Comparative Education Review 57:285–308.
Cunha, Flavio, and Heckman, James. 2007. “The Technology of Skill Formation.” American Economic Review 97:31–47.
Brunello, Giorgio, and Daniele Checchi. 2007. “Does School Tracking Affect Equality of Opportunity? New International Evidence.” Economic Policy 22:781–861.
d’Hombres, Beatrice, Francesca Borgonovi, and Bryony Hoskins. 2010. “Voter Turnout, Information Acquisition and Education: Evidence from 15 European Countries.” The BE Journal of Economic Analysis & Policy 10:1–34.
DiPrete, Thomas A. and Buchmann, Claudia. 2013. The Rise of Women: The Growing Gender Gap in Education and What It Means for American Schools. New York: Russell Sage Foundation.
Grätz, Michael. 2021. “Does Increasing the Minimum School-Leaving Age Affect the Intergenerational Transmission of Education? Evidence from Four European Countries.” European Sociological Review, DOI: 10.1093/esr/jcab065.
Meghir, Costas, and Mårten Palme. 2005. “Educational Reform, Ability, and Family Background.” American Economic Review 95:414–24.
Sammons, Pamela. 1995. “Gender, Ethnic and Socio‐Economic Differences in Attainment and Progress: A Longitudinal Analysis of Student Achievement over 9 years.” British Educational Research Journal 21:465–85.
Scheeren, Lotte. 2022. “The Differential Impact of Educational Tracking on SES Gaps in Educational Achievement for Boys and Girls.” European Sociological Review, DOI:10.1093/esr/jcac012.
Shavit, Yossi and Westerbeek, Karin. 1998. “Reforms, Expansion, and Equality of Opportunity.” European Sociological Review 14:33–47.
Sørensen, Aage B. 1970. “Organizational Differentiation of Students and Educational Opportunity.” Sociology of Education 43:355–76.
Österman, Marcus. 2021. “Can We Trust Education for Fostering Trust? Quasi-Experimental Evidence on the Effect of Education and Tracking on Social Trust.” Social Indicators Research 154:211–33.
van de Werfhorst, Herman G. 2018. “Early Tracking and Socioeconomic Inequality in Academic Achievement: Studying Reforms in Nine Countries.” Research in Social Stratification and Mobility 58:22–32.
van de Werfhorst, Herman G. 2019. “Early Tracking and Educational Social Inequality in Educational Attainment: Educational Reforms in 21 Countries.” American Journal of Education 126:65–99.
van de Werfhorst, Herman G., and Jonathan J. B. Mijs. 2010. “Achievement Inequality and the Institutional Structure of Educational Systems: A Comparative Perspective.” Annual Review of Sociology 36:407–28.
05. Children and Youth at Risk and Urban Education
Paper
Effects of ability grouping on equity – Evidence from the Czech Republic
Jana Strakova, Jaroslava Simonova
Charles University in Prag, Czech Republic
Presenting Author: Strakova, Jana
The Czech education system is characterised by early tracking. It tracks students officially at the age of 11, when about 10 % of students transit to so-called multi-year grammar schools. There are, however, also different forms of hidden tracking – specialised classes that often track children from the first grade of primary school. Most often these are classes with extended English teaching or bilingual classes. In some of these classes parents pay extra fees, even though they are part of the public system. There is a lot of evidence that both the multi-year grammar schools and specialised classes are primarily tools of social selection that enable educated and financially well-off parents to secure superior educational opportunities for their children in the public education system. Tracking is, however, supported not only by these parents, but also by many teachers, as they believe that in a more homogeneous environment they are able to provide better services to all students. In an attempt to meet the needs of these parents and teachers and at the same time to respond to criticism that tracking contributes to the deepening of educational inequalities, many schools are now starting to sort and group children according to their performance for the main subjects within the grades. The aim of the research presented in this paper is to investigate this type of ability grouping in Czech schools.
From research carried out mainly in Anglo-Saxon countries (e.g. Gamoran & Mare, 1989; Gamoran, 1992; Kerckhoff, 1986; Slavin, 1990; Hanushek & Woessman, 2005) there is ample evidence that tracking based on the cognitive abilities of pupils does not improve overall educational results, but increases the differences between pupils from higher and lower tracks. Pupils in higher tracks achieve better results than they would achieve in a comprehensive system and pupils in lower tracks the opposite. Research studies further show that the distribution into tracks is not fair: parents who care about their children’s education are able to ensure elective studies for their children even in a situation where they would not be accepted for these on the basis of their achievement. Research also shows that in higher tracks pupils have better conditions for education: better equipment, better teachers, and a more favourable learning climate. All this increases the injustice of differentiated systems (Gamoran & Nystrand, 1991; Oakes, 2005).
Studies comparing tracking, in which pupils are divided in some phase of their studies and continue to study permanently in differentiated classes, and ability grouping, where pupils are divided according to ability only into the main subjects across grades and spend the rest of their school time in heterogeneous classes, agree that the latter has a lower impact on inequality. Some studies report that ability grouping increases the average achievement, i.e. it is beneficial for students in all groups (e.g. Slavin 1987, Steenbergen-Hu et al., 2016). However, most studies agree that pupils in the low-ability group achieve worse results than they would in a heterogeneous setting because having high-achieving classmates is associated with increased achievement (Ireson et al., 2005; Francis et al., 2017; Saleh et al., 2005; Scholfield, 2010) and that the students in lower groups develop a lower self-concept (Ireson & Hallam, 2009).
The goal of this research is to contribute to knowledge about the implementation of ability grouping with evidence from Czech schools. The study seeks answers to the following research questions: To what extent do the quality of teaching, the curriculum presented, the demands placed on pupils, and the learning climate differ in individual groups? Are the differences between groups or subjects consistent or do they differ between schools/subjects?
Methodology, Methods, Research Instruments or Sources UsedThe research design was a multi-case study of five basic schools (basic schools include five years of primary and four years of lower secondary education) which apply ability grouping in at least two main subjects. The research took place in a city school in the capital city with 600 pupils, a suburban school on the outskirts of the capital with 780 pupils, a satellite school in a small town within driving distance of the capital with 870 pupils, a rural school in a relatively poor region in the western part of the country with 170 pupils, and a small town school with 450 pupils in a rich region in the eastern part of the country.
The research focused on the sixth or seventh grade, depending on the grade in which ability grouping occurred. Ability grouping took place in English, Czech, and mathematics, while some schools differentiated only in mathematics and English.
School documents (including the school’s curriculum, timetables, and information on intra-group transfers) and test results from tests administered by a private agency that offers feedback testing to schools were analysed. Furthermore, in-depth interviews were conducted with the school management, with teachers teaching in ability groups, and with three to five representatives of pupils from individual groups. In schools, lessons of the main subjects were videorecorded, with three consecutive lessons in each group being recorded, with all the groups recording at the same time (within two weeks).
The interviews were recorded, transcribed, and coded in the MAXQDA software using open coding. The recordings of lessons were coded using the International Comparative Analysis of Learning and Teaching (ICALT) observation instrument, which was enriched with an additional set of subject-specific codes. Coding was performed by two or three independent coders, who then unified their evaluations. The coder agreement for individual lessons was 85-95 percent.
The data was analysed within individual cases, followed by a cross-case analysis.
Conclusions, Expected Outcomes or FindingsAt the time of the submission of the abstract, only preliminary analyses were available. The schools that were monitored made great efforts to eliminate the negative consequences of ability grouping. They consistently ensured the achievement of the same educational goals in all groups and tried to achieve fair grading so that pupils’ grades were not affected by belonging to any of the groups. The groups were consistently labelled in such a way that the label did not evoke a higher value for any of the groups. Great attention was also paid to the division into groups in such a way as to best correspond to the results and potential of the pupils. The majority of the pupils and teachers perceived ability grouping positively.
However, it turns out that factors other than the students’ cognitive results still play a role in the assignment of pupils to groups, e.g. parents’ attempts to place pupils in a less advanced group so that they have better grades on their report cards, which will subsequently help them when transferring to secondary school; failure in the placement test; the wish of the pupils to stay in the same group as their friends. Transfers between groups are possible but occur exceptionally.
The analyses of the lessons showed that there are great differences between individual groups in terms of demands, methods, and forms of teaching. From this evidence it is clear that a child’s placement has a huge impact on their learning experience.
ReferencesFrancis, B., Archer, L., Hodgen, J., Pepper, D., Taylor, B., & Travers, M.-C. 2017. Exploring the relative lack of impact of research on ‘ability grouping’ in England: a discourse analytic account. Cambridge Journal of Education, 47 (1), 1-17.
Gamoran, A. 1992. Synthesis of Research/Is Ability Grouping Equitable? Educational Leadership, 50 (2), 11-17.
Gamoran, A., & Nystrand, M. 1991. Background and Instructional Effects on Achievement
in Eighth-Grade English and Social Studies. Journal of Research on Adolescence 1 (3),
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Gamoran, A., & Mare, D. R. 1989. Secondary School Tracking and Educational Inequality: Compensation, Reinforcement, or Neutrality? The American Journal of Sociology 94 (5), 1146-1183.
Hanushek, E. A., & Woessman, L. 2005. Does Educational Tracking Affect Performance and Inequality? Differences-in-Differences Evidence across Countries. Ifo Working Paper No. 1
Ireson, J., Hallam, S., & Hurley, C. 2005. What are the effects of ability grouping on GCSE attainment? British Educational Research Journal, 31 (4), 443-458.
Ireson, J., & Hallam, S. 2009. Academic self-concepts in adolescence: Relations with achievement and ability grouping in schools. Learning and Instruction, 19 (3), 201-213.
Kerckhoff, A. C. 1986. Effect of Ability Grouping in British Secondary Schools. American Sociological Review 51, 842-858.
Oakes, J. 2005. Keeping Track: How Schools Structure Inequality (2nd ed.). New Haven, CT: Yale University Press.
Saleh, M., Lazonder, A., & De Jong, T. 2005. Effects of within-class ability grouping on social interaction, achievement, and motivation. Instructional Science, 3 (2), 105-119.
Schofield, J. W. 2010. International evidence on ability grouping with curriculum differentiation and the achievement gap in secondary schools. Teachers College Record, 112 (5), 1492-1528.
Slavin, R. E. 1990. Achievement Effects of Ability Grouping in Secondary Schools: A Best-Evidence Synthesis. Review of Educational Research 60 (3), 471-499.
Steenbergen-Hu, S., Makel, M.C., & Olszewski-Kubilius, P. 2016. What One Hundred Years of Research Says About the Effects of Ability Grouping and Acceleration on K–12 Students’ Academic Achievement: Findings of Two Second-Order Meta-Analyses Review of Educational Research, 86 (4), 849-899.
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