Conference Agenda

The proposed symposium is based on the quantitative data analysis output stemming from the Horizon 2020 PIONEERED project “Pioneering policies and practices tackling educational inequalities in Europe” (https://www.pioneered-project.eu). The project encompasses nine European countries and aims to uncover the mechanisms behind the persisting educational inequalities, as well as to offer science-based policy advice.

Empirical findings presented here are a part of a larger analytical work based on the exploration of national and cross-national quantitative data focusing on inequalities throughout the various educational stages (primary, secondary, and tertiary), including the trajectories and transitions, as well as both the formal and non-formal/informal educational settings. To bring together the drivers of educational inequalities and the complexity of their interactions on the one hand, and varying education systems on the other, the analyses rely on a comprehensive methodological framework proposed in the project, which combines the multi-level, intersectional, and life-course perspectives (abbr. MILC). The Multi-level approach determines the contribution of socio-economic and educational policies (macro-) through the schools, neighborhoods (meso-), and individual and family circumstances (micro-level) on educational outcomes. Individual-level predictors, such as socio-economic origin, gender, migration background are well-known axes of inequality. However, as we empirically show, it is their combinations, i.e. an intersection of these axes, that have a differentiated impact on educational outcomes. Thus, the Intersectional approach provides a nuanced understanding of the importance of each of this axis for a specific outcome, as well as across educational careers and contexts (e.g. between countries). The Life-course approach suggests that educational inequalities occur in cumulative manner, leading to a particular individual school path.

The three contributions in this symposium refer to these (or a combination of) approaches when answering research questions related to drivers of inequalities such as school admission age, school segregation and composition, and sense of belonging.

The first presentation analyses the effect of age cut-off at the beginning of school on achievement at the start and later along the educational path in the North-Western part of Switzerland. This is an example of school policy (at the macro-level), where an arbitrary decision with respect to cut-off age for school admission, has a concrete effect on student’s achievement that extends beyond the primary education (cumulative disadvantage). While empirical explorations exist in the US, this study is among very few that investigates this question in European context. The study is important as the unique data allows analyzing the achievement gap over time (life-course). Additionally, given the high-stratification of the education system, this study provides key evidence on the relevance of age cut-off on track placement in secondary education.

The second presentation focuses on school-segregation and the long-term consequences for achievement and attainment in Luxembourg, that is known for its multilingual and highly-stratified education system with a large share of immigrant students. Following the multi-level, intersectional and life-course approaches, the authors empirically test the effect of primary school composition on achievement (math and German language) and attainment (academic vs other school tracks) in secondary education. The emphasis of the paper is on three major inequality axes: social origin, migration status and gender.

The third presentation studies how the sense of belonging within learning environment varies across educational stages: primary, secondary and tertiary, focusing on the same inequality axes mentioned above (intersectional approach). This is a comparative study with more than 30 countries that attempts to explain the variation among concerned groups by including key macro-level indicators (multi-level approach). While sense of belonging is less frequently studied in the realm of educational outcomes, existing studies have largely proved its importance for school success and a general well-being of young children and youth.

Pupils who did not start learning at the same level as their peers might subsequently fall behind throughout their educational careers (e.g., Heckman 2006; Passaretta et al. 2022). The modalities of compulsory school admission may contribute to the emergence of early gaps in educational performance. Nearly all education systems have arbitrarily chosen cut-off dates for school enrolment, which create age differences of up to a year within a cohort of pupils. Prior research has shown that the youngest pupils in a cohort fall behind their relatively older peers in educational performance (e.g., Bedard and Dhuey 2006; Peña 2017; Dicks and Lancee 2018). These performance gaps are coined as relative age effects, which can be framed within theories of cumulative (dis)advantages (e.g., DiPrete and Eirich 2006). Drawing on a comprehensive data set encompassing the entire student population in North Western Switzerland (BR NWCH 2021), the study addresses three research questions. First, it is investigated to what extent pupils’ relative age affects their educational achievement in different subjects and track placement in secondary education. Second, by exploiting the longitudinal structure of the data, it is examined whether the influence of relative age diminishes the course of educational trajectories. Third, the study establishes a record linkage between administrative data and pupils’ test data to investigate whether pupils from disadvantaged backgrounds suffer more strongly from relative age effects. A pupil’s relative age might be correlated with various unobserved factors. Two strategies are employed to address these endogeneity concerns. First, the study employs an instrumental variable approach using “assigned relative age” (e.g., Bedard and Dhuey 2006) as an instrument for pupils’ actual age. Second, the study uses a regression discontinuity design contrasting pupils born just before and after the cut-off date to estimate relative age effects in Switzerland. Preliminary results provide evidence that students with a relative age advantage when they entered school achieve significantly higher than their counterparts with a relative age disadvantage during their first years of primary education. However, relative age effects vanish the more students advance in their educational trajectory. Additional analyses shed light on potential effect heterogeneity. The study illustrates how early disadvantages emerge by chance through arbitrarily chosen cut-off dates for school eligibility. Scholars and policy-makers alike are urged to debate how the modalities of school entry can be designed to ensure equal starting conditions for all.

Bedard, K., & Dhuey, E. (2006). The persistence of early childhood maturity: International evidence of long-run age effects. The Quarterly Journal of Economics, 121(4), 1437–1472. BR NWCH. (2021). Checks in BR NWCH 2013-2020 [Dataset]. University of Zurich, Institute for Educational Evaluation. Distributed by FORS, Lausanne. https://doi.org/10.23662/FORS-DS-1261-1 Dicks, A., & Lancee, B. (2018). Double disadvantage in school? Children of immigrants and the relative age effect: A regression discontinuity design based on the month of birth. European Sociological Review, 34(3), 319–333. DiPrete, T. A., & Eirich, G. M. (2006). Cumulative advantage as a mechanism for inequality: A review of theoretical and empirical developments. Annual Review of Sociology, 32, 271-297. Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science 312(5782), 1900-1902. Passaretta, G., Skopek, J., & van Huizen, T. (2022). Is social inequality in school-age achievement generated before or during schooling? A European perspective. European Sociological Review, 38(6), 849-865. Peña, P. A. (2017). Creating winners and losers: Date of birth, relative age in school, and outcomes in childhood and adulthood. Economics of Education Review, 56, 152–176.

For several decades, sociological research has studied determinants of educational inequalities, whereby most researches have focused on individual students’ characteristics (e.g., Boudon, 1974; Bourdieu, 1984), though others also considered system variables such as school composition and segregation (e.g., Jencks, 1974). However, few studies have addressed the possible interaction of system and student characteristics in relation to student academic outcomes (Gross et al., 2016). Educational inequalities in Luxembourg – with a highly stratified, multilingual education system, further characterised by a large proportion of students with a 1st or 2nd generation migrant status - are related to student characteristics (i.e., socio-economic status and migration status) (e.g., Lenz & Heinz, 2018) as well as schools’ social composition (Martins & Veiga, 2010). The present study aimed to investigate especial the intersectional impact of students´ academic and socio-demographic characteristics, school composition and school tracks on students’ academic performance in Luxembourg. It draws on longitudinal data collected as part of the Luxembourg school monitoring programme “Épreuves Standardisées” (ÉpStan; Fischbach et al., 2014) and included all students enrolled in public education Grade 3 (November 2013) matched with data from the same students in Grade 9 (November 2017-2021) including those repeating once or twice (N≈3600). Results of multilevel mixed effects regression analyses show that both Math and language achievement in Grade 9 is affected by student characteristics (gender, SES, migration background and prior achievement), as well as by the school track and school composition (i.e., percentage of Low SES families in 3rd Grade). In addition, some cross-level interaction effects were found. For example, results show that after controlling for prior performance and other individual characteristics, the gender gap in math achievement is more pronounced in the higher than in the middle school track. These results indicate that not only student and system variables, but also their intersectionality affect student achievement outcomes. More specifically, accounting for socio-demographic student characteristics and prior achievement, our results demonstrate a long-term effect of school composition on students´ educational pathways. Student and system characteristics have a direct effect on academic achievement as well as an indirect effect via school tracking. Furthermore, student and system variables interact such that achievement differences between certain groups of students (e.g., boys) may be exacerbated by system characteristics (i.e., school composition). Results will be discussed in relation to theory as well as their possible implications for tailored policy making.

Boudon, R. (1974). Education, opportunity and social inequality: changing prospects in Western society. Wiley. Bourdieu, P. (1984). Distinction: A social critique of the Judgement of taste (translated by R. Nice). Harvard University Press. Fischbach, A., Ugen, S., & Martin, R. (2014). ÉpStan Technical Report. University of Luxembourg ECCS research unit/LUCET. www.epstan.lu Gross, C., Gottburgsen, A., & Phoenix, A. (2016). Education systems and intersectionality. In A. Hadjar & C. Gross (Eds.), Education systems and inequalities (pp. 51–72). Policy Press. Jencks, C. (1974). Inequality: A re-assessment of the effect of family and schooling in America. Lane. Lenz, T., & Heinz, A. (2018). Das Luxemburgische Schulsystem: Einblicke und Trends. In T. Lentz, I. Baumann, & A. Küpper. (Eds.), Nationaler Bildungsbericht Luxemburg 2018 (pp. 22–34). Université du Luxembourg (LUCET) & SCRIPT. Martins, L., & Veiga, P. (2010). Do inequalities in parents’ education play an important role in PISA students’ mathematics achievement test score disparities? Economics of Education Review, 29(6), 1016–1033. https://doi.org/10.1016/j.econedurev.2010.05.001

Sense of belonging in education has been linked to many aspects of students’ overall success in their educational path. It is associated with academic achievement and well-being related to studies (Finn & Zimmer 2012; Ulmanen et al. 2016; Pedler et al. 2022). Sense of belonging is also among humans’ basic psychological needs (Maslow 1943; Wenger 1998; Ryan & Deci, 2000). Gender, socioeconomic status, migrant status, and educational context has been linked with the variation in the sense of belonging (OECD, 2017), but the research has not been systematic or conclusive. While research often focuses on individual dimensions of inequalities, the effects of sociodemographic factors are intertwined and an intersectional approach is required in order broaden the view (e.g., Codiroli Mcmaster & Cook, 2019). The present study takes an intersectional view on the sense of belonging along the educational path. Interconnections between sociodemographic and contextual aspects are explored. The data consists of large, cross-national datasets, namely TIMSS, PIRLS, PISA, and Eurostudent, reflecting different educational stages, namely primary, secondary and tertiary education. The datasets have been harmonized for comparability across educational stages (see Kroezen & Alieva, 2022). Gender and socioeconomic and migrant statuses have been used to reflect intersectional inequalities, and macro-level indicators have been used in exploring associations between the educational context and inequalities. These include the Tracking index, UNESCO’s Female percentage of the graduation ratio from ISCED 6/7 in tertiary education, and the Migrant Integration and Policy Index. The findings show that the dynamics that play behind the sense of belonging vary in different stages of educational path. While girls perceive stronger belonging in education in primary level compared with boys, the socioeconomic and migrant statuses become more essential in inequalities in the sense of belonging in secondary and tertiary levels. Associations between educational context and intersectional inequalities in the sense of belonging are complex and even counterintuitive. While tracking seems to have little effect on intersectional inequalities, the analyses revealed mixed effects regarding the proportion of female graduates and inclusiveness towards immigrants. The measures that are intended for equalizers may even be counterproductive or they can benefit unintended groups. The findings indicate that individuals’ sense of belonging is not stable in different educational contexts, but rather a variety of individual and contextual factors are related to it.

Codiroli Mcmaster, N. & Cook, R., 2019. The contribution of intersectionality to quantitative research into educational inequalities. Review of Education, 7 (2), 271–292. Finn, J.D. & Zimmer, K.S., 2012. Student Engagement: What Is It? Why Does It Matter? In: S.L. Christenson, A.L. Reschly, and C. Wylie, eds. Handbook of Research on Student Engagement. New York: Springer. Kroezen, T. & Alieva, A., 2022. PIONEERED: Data Harmonisation Guidelines. Deliverable No. 4.1. Zenodo. Maslow, A.H., 1943. A theory of human motivation. Psychological Review, 50 (4), 370–396. OECD, 2017. PISA 2015 Results (Volume III): Students’ Well-Being. OECD. Pedler, M.L., Willis, R., & Nieuwoudt, J.E., 2022. A sense of belonging at university: student retention, motivation and enjoyment. Journal of Further and Higher Education, 43 (3), 397–408. Ryan, R.M. & Deci, E.L., 2000. Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. Contemporary Educational Psychology, 25 (1), 54–67.