Im Rahmen der Vortragsreihe „Integration und Migration“, die vom InZentIM gemeinsam mit dem Institut für Soziologie (IfS) und dem Käte Hamburger-Kolleg: Centre for Global Cooperation Research (KHK/GCR21) ausgerichtet wird, freuen wir uns, den kommenden Vortrag anzukündigen:
Towards a better understanding of the multilayered reality of linguistic inequalities in education
Orhan Agirdag (KU Leuven / University of Amsterdam)
09.07.2019, 16-18 h
Campus Essen, S05 T00 B59
While a growing number of European studies investigate educational inequalities across socioeconomic and migration background lines, less attention is given to linguistic group disparities. This is surprising as the achievement gap between language minority (LM) students and native-speaking (NS) students is at least as large as the achievement gap between immigrants and native-borns. Previous studies show that LM students perform worse than NS students, even when SES and migration background is taken into account. However, the existing studies are restricted at all levels of interest. At micro-level, it is not clear which specific elements of language use trigger underachievement. I will look at different aspects of language use (such as the use at home or school context) and examine how these specific aspects are related to academic achievement. At meso-level, the dynamics between school policy and linguistic inequalities are largely ignored. However, as the number of LM students increases, many schools tend to answer the growing linguistic diversity with a monolingual reflex by discouraging the use of the mother tongue. Nevertheless, the way schools and teachers deal with linguistic diversity might have implications for students’ academic achievement and sense of school belonging. At macro-level, the increasing number of cross-national studies paid little attention to linguistic inequalities. I examine whether the level of linguistic diversity in a country and some characteristics of the educational system are related to linguistic inequalities in education.
Methodologically, I use different data sets such as the PISA 2012 and 2015 data for the cross-national analyses, and the Flemish Validiv-data for a within-country analysis. The analyses are mainly based on multilevel regression analysis.
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