04 SES 06 D, Effects Of Social Inequalities On Inclusive Education
An inclusive school system aims to provide equal opportunities for education and satisfaction of individual learning needs to all students. Like in other cantons in Switzerland and in many other countries, the education system in the canton of Bern provides integrative measures in regular schools for children and adolescents with special educational needs. These measures include adjustments of learning objectives (mostly “reduced individual learning objectives”; RILO) and compensation for disadvantages (CFD). While the target group for RILO are students with generally low academic performance who are not able to achieve the regular learning objectives[II1] , the target group of CFD are students with an at least average academic performance but with a specific disadvantage (e.g. Dyslexia or ADHD) which is compensated by special aids (e.g. spell checker program, extra time at exams) to enable them to achieve the regular learning objectives. In contrast to RILO, CFD does not need to be mentioned in the students report, but diagnostic clarification of a specialist is needed in most cases. While these measures are intended to support students with special education needs, they also bear the risk to reproduce or enhance educational inequalities, for example by conveying potential stigmatizing labels (“being a RILO-student”) or when the likelihood of receiving more (CFD) or less (RILO) “favorable” measures is also associated with ascriptive characteristics (e.g., socioeconomic background of the students) rather than with cognitive ability and school performance alone. Research in the field of educational tracking has already shown that characteristics such as migration background or social origin may influence the allocation to certain tracks and that school performance is not the only criteria for educational success (Becker & Beck, 2012; Ditton & Krüsken, 2009; Goldthorpe, 2003; Kronig, 2003; Reisel, 2011; Stocké, 2007). For example, in case of unexpected failure of their children at school, parents from higher social classes use a range of strategies to influence the performance assessment of teachers (Becker, 2000; Ditton, 2007; Stocké, 2010). Similar mechanisms may be expected when it comes to the allocation of individual measures associated with special needs, because parents’ view and consent needs to be considered in the allocation process. In a previous research project (SECABS study), the authors investigated the distribution of these measures at the end of primary school and found that socioeconomic background did indeed predict the allocation of CFD in primary school: children from families with high socioeconomic status were two to three times more likely to receive this measure than children from families with lower socioeconomic status, despite controlling for cognitive ability and school performance (Sahli Lozano, Ganz & Wüthrich, 2018). Further, it was shown that teacher’s expectancy of cognitive ability (prediction of IQ) of children receiving the measure of RILO were negatively biased and that teachers systematically underestimated IQ-scores of the children (Greber, Sahli Lozano & Steiner, 2017). Therefore, different measures may have different consequences for educational success in the long term. The present follow-up project investigates these long-term effects. It is based on the previous sample of primary school children and follows them into secondary school to assess development trends associated with these different measures three years later. The study aims to answer the following research questions: How are integrative measures distributed in secondary school, and will similar patterns of biased allocation based on socioeconomic background emerge? How did school performance of children receiving a given measure (RILO or COD) in primary school develop compared to matched pairs without such measures during these three years? As data collection has just finished, no results are available yet, but the authors will be able to present first results at the ECER 2019.
Data collection took place in a sample of 113 secondary school classes from 53 schools in the canton of Bern, with a total sample of 2026 students. This sample consists of previous 5th / 6th grade students that took part in the SECABS study three years ago. Because new classes are created in the transition from primary to secondary school, roughly 25% of the sample are students that were screened during the SECABS study. As in the SECABS study before, students completed standardized tests in math and language (German) to assess school performance level, as well as a general intelligence test (Culture Fair Intelligence Test 20-R). In addition, information about individual measures of each student (RILO, NAG or no measure) and parents socio-economic background (highest international socioeconomic index of occupational status; Ganzeboom & Treiman, 1996) was collected. Of the total sample of 2026 students, 70 were identified as receiving RILO and 54 as receiving COD. To analyze whether socioeconomic status has an influence on the allocation of the two measures RILO and COD, separate multilevel level logistic regressions will be performed while controlling for variables such as school performance and general intelligence. Further, developments in school performance during the 3 years will be assessed for students that already received the measure RILO or COD in 5th / 6th grade. Using propensity score matching, control groups of students with similar characteristics but who do not receive the measure will be compared against students with RILO or COD to assess how these measures relate to school performance development.
Based on the previous findings (Sahli Lozano et al., 2018), we hypothesize that the allocation of COD (but not necessarily RILO) is influenced by socioeconomic background of the students. Further we hypothesize that students who received RILO in primary school will have poorer academic outcomes when compared to students who were similar in academic performance and IQ, but who did not receive the measure. We also hypothesize analog findings for students with COD, but in the opposite direction. We aim to discuss the presence or absence of the hypothesized outcomes based on labeling effects (e.g. Higgins, Raskind, Goldberg & Herman, 2002; Greber et al. 2017) and sociological theories dealing with the influence of social status on educational success (Boudon, 1974; Bourdieu, 1983). These measures and their allocation process are somewhat specific to the canton of Bern in Switzerland, but the findings and the discussion about opportunities and risks of integrative measures in general and how to best ensure fair allocation of such measures will be interesting and relevant for an international audience, as similar measures exist in other countries as well.
Becker, R. (2000). Klassenlage und Bildungsentscheidungen. Eine empirische Anwendung der Wert-Erwartungstheorie. KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie, 52(3), 450–474. http://doi.org/10.1007/s11577-000-0068-9 Becker, R., & Beck, M. (2012). Herkunftseffekte oder statistische Diskriminierung von Migrantenkindern in der Primarstufe? In R. Becker, & M. Beck, (Hrsg.), 2012: Soziologische Bildungsforschung. Sonderheft 52 der Kölner Zeitschrift für Soziologie und Sozialpsychologie. Wiesbaden: VS Verlag für Sozialwissenschaften. Boudon, R. (1974). Education, opportunity, and social inequality : changing prospects in Western society. New York: Wiley. Bourdieu, P. (1983). Ökonomisches Kapital, kulturelles Kapital, soziales Kapital. In R. Kreckel (Hrsg.), Soziale Ungleichheiten. Soziale Welt, Sonderband 2 (S. 183–198). Göttingen: O. Schwartz. Ditton, H. (2007). Kompetenzaufbau und Laufbahnen im Schulsystem : Ergebnisse einer Längsschnittuntersuchung an Grundschulen. Münster New York München Berlin: Waxmann. Ditton, H., & Krüsken, J. (2009). Bildungslaufbahnen im differenzierten Schulsystem. Entwicklungsverläufe von Bildungsaspirationen und Laufbahnempfehlungen in der Grundschulzeit. In J. Baumert, K. Maaz, & U. Trautwein (Hrsg.), Bildungsentscheidungen : Zeitschrift für Erziehungswissenschaft Sonderheft 12 / 2009 (S. 74–102). Wiesbaden: VS Verlag für Sozialwissenschaften. Ganzeboom, H. B. G., & Treiman, D. J. (1996). Internationally Comparable Measures of Occupational Status for the 1988 International Standard Classification of Occupations. Social Science Research, 25, 201–239. Goldthorpe, J. (2003). The myth of education-based meritocracy. New Economy, 10(4), 234–239. http://doi.org/10.1046/j.1468-0041.2003.00324.x Greber, L., Sahli Lozano, C., & Steiner, F. (2017). Lehrpersoneneinschätzungen von Kindern mit integrativen schulischen Massnahmen. Empirische Pädagogik, 31(3), 303–322. Higgins, E. L., Raskind, M. H., Goldberg, R. J., & Herman, K. L. (2002). Stages of Acceptance of A Learning Disability: The Impact of Labeling. Learning Disability Quarterly, 25(1), 3–18. https://doi.org/10.2307/1511187 Kronig, W. (2003). Das Konstrukt des leistungsschwachen Immigrantenkindes. Zeitschrift für Erziehungswissenschaft, 6(1), 126–141. http://doi.org/10.1007/s11618-003-0008-3 Reisel, L. (2011). Two Paths to Inequality in Educational Outcomes: Family Background and Educational Selection in the United States and Norway. Sociology of Education, 84(4), 261–280. Sahli Lozano, Caroline; Ganz, Anne-Sophie & Wüthrich, Sergej. Systematic Randomness in the Allocation of Integrative School Measures. Presentation held at European Conference of Educational Research (ECER). Bozen, 06.09.2018. Stocké, V. (2007). Explaining Educational Decision and Effects of Families’ Social Class Position: An Empirical Test of the Breen–Goldthorpe Model of Educational Attainment. European Sociological Review, 23(4), 505–519. http://doi.org/10.1093/esr/jcm014
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