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Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

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Session Overview
Session
28 SES 17 A: Schools from Inside
Time:
Friday, 25/Aug/2023:
3:30pm - 5:00pm

Session Chair: Felix Büchner
Location: Gilbert Scott, Randolph [Floor 4]

Capacity: 80 persons

Paper Session

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Presentations
28. Sociologies of Education
Paper

Unpacking Socio-Digital Inequalities in Everyday Schooling through Ordinalization: Scenes from Swedish and German Classrooms.

Felix Büchner1, Svea Kiesewetter2

1University of Oldenburg, Germany; 2University of Gothenburg, Sweden

Presenting Author: Büchner, Felix; Kiesewetter, Svea

Public education has increasingly become a domain for nations and supranational entities to push for digitalization, which is often accompanied with promissory visions aiming at improvements in terms of e.g. ensuring more equitable schooling through digital technologies and infrastructures (European Commisson 2020). However, digitalization also potentially reconfigures 'socio-digital inequalities' (Helsper 2021) in schools, which are "systematic differences between individuals from different backgrounds in the opportunities and abilities to translate digital engagement into benefits and avoid the harm that might result from engagement with ICTs". Following this line of argument, socio-digital inequalities play out and are reconfigured depending on the context as digital infrastructures, equipment, curricula, teaching/learning strategies and competences found in practice vary greatly among and within nations. This study aims to provide situated and local accounts of the unfolding of socio-digital inequalities in practice in two economically and technologically strongly positioned nations, Sweden and Germany. It therefore contributes to current discussion points in critical educational technology research, where the roles of educational platforms, algorithms, infrastructuring or datafication practices in relation to the re/production of inequality are increasingly questioned.

Sweden and Germany both position themselves as technological and digital 'pioneers' in the European community of nations and consider digitalization as positive and a way to address inequalities (Ferrante et al. 2023 under review). However, how that might manifest in local practices differs, as the schooling landscapes vary greatly: In Sweden school digitalisation has unfolded as part of a marketization that includes free school choice and for-profit schools funded by the state that run alongside existing municipally run schools (Svallfors & Tyllström 2019). Educational technologies, platforms and software are generally procured but provided by commercial actors, similar to (data) infrastructures. Overall, this has led to increasing concerns about segregation and inequality despite generally well-resourced schools (Ljungqvist & Sonesson 2021). In Germany on the other hand, digitalization has traditionally been focused on privacy concerns and an orientation to open-source solutions, that are built rather than bought (Macgilchrist 2019). Even after German schools started inviting more commercial actors and their digital products in the context of the coronavirus pandemic, the digitalization of schooling remains a slow procedure because of underfunding and the federal organization of the education system (Cone et al. 2021). Structurally, Germany has been criticised as having the most unequal education system in Europe, due to its tripartite school system that can block social mobility. Therefore, Sweden and Germany are rich contexts for this study to unpack the local and nuanced unfolding of socio-digital inequalities in practice.

However, inequalities are difficult to grasp, and as previous research has highlighted, they are difficult to approach ethnographically (Emmerich und Hormel 2017). Therefore, socio-digital inequalities in this study are approached through Marion Fourcade's account of different 'classificatory judgements' (Fourcade 2016) which serves as guiding lens. According to Fourcade, classification processes have different qualities and can be understood as either cardinal, nominal or ordinal classifications. While cardinal classifications refer to the numeral value of things (and are of lesser importance in this study), nominal classifications aim at the essence of things in a horizontal distinction and ordinal classifications refer to the value of things in a vertical, hierarchical distinction. With the help of this conceptual framing, everyday school practices could be observed and analysed in ethnographic field research, especially with regard to which 'differences' (in the sense of nominalisation) they produce between actors and which 'inequalities' (in the sense of ordinalization) result from them.

Accordingly, this paper asks firstly how digital technology is encountered in Swedish and German school practice and secondly how such practices relate to re/productions of socio-digital inequalities.


Methodology, Methods, Research Instruments or Sources Used
The paper is based on ethnographic research stays in schools in a municipality/ federal state in Sweden and Germany. The schools were selected according to the above-mentioned structural conditions of the respective education systems in order to reflect the greatest possible diversity of prerequisites and conditions with regard to e.g. digital infrastructure or socio-economic background of students. Data, in the form of observations, field notes, informal and semi-structured interviews were generated over the course of nine months, resulting in a total of 122 observed lessons and a total of 25interviews with teachers, headmasters, school social workers, IT administrators, municipal IT developers and students. These varied approaches helped to capture diverse perspectives on the topic of digital education and inequality and to contextualise the classroom observations.
Furthermore, the concept of ‘rich points’ was used to navigate the ethnographic field. Michael Agar describes rich points as "signal[s] of a difference between what you know and what you need to learn to understand and explain what just happened" (Agar 2006, 64). Accordingly, they are moments of surprise, irritation or fascination during ethnographic research that cannot be explained at first and for this very reason were understood and employed as analytical access points to the generated data.

Conclusions, Expected Outcomes or Findings
In the paper, we provide access points into local and nuanced re/configurations of socio-digital inequalities in technologically and economically well positioned countries. By providing 'scenes' (Emerson, Fretz, und Shaw 2011) from the ethnographic fieldwork in Sweden and Germany along thick descriptions, the study provides insight into the ways in which socio-digital inequalities are re/produced in everyday school practices through classificatory judgements and ordinalization, further highlighting how these local practices are related to the diversity of infrastructures, actors and processes found in the field. The findings contest the overall assumption of digital technology being a magic bullet for socio-digital inequalities by contrasting the two national contexts on the one hand, but also aim at contrasting the cases within the national contexts, thus drawing a complex picture of the diversity of European digital educational practices and their interconnectedness with re/producing social-digital inequality.
References
Cone, Lucas, Katja Brøgger, Mieke Berghmans, Mathias Decuypere, Annina Förschler, Emiliano Grimaldi, Sigrid Hartong, u. a. 2021. „Pandemic Acceleration: Covid-19 and the Emergency Digitalization of European Education“. European Educational Research Journal, September, 147490412110417. https://doi.org/10.1177/14749041211041793.
Emerson, Robert M., Rachel I. Fretz, und Linda L. Shaw. 2011. Writing Ethnographic Fieldnotes, Second Edition. 2nd revised edition. Chicago: University of Chicago Press.
Emmerich, Marcus, und Ulrike Hormel. 2017. „Soziale Differenz und gesellschaftliche Ungleichheit: Reflexionsprobleme in der erziehungswissenschaftlichen Ungleichheitsforschung“. In Differenz - Ungleichheit - Erziehungswissenschaft: Verhältnisbestimmungen im (Inter-)Disziplinären, herausgegeben von Isabell Diehm, Melanie Kuhn, und Claudia Machold, 103–21. Wiesbaden: Springer Fachmedien. https://doi.org/10.1007/978-3-658-10516-7_6.
European Commisson. 2020. „Digital Education Action Plan 2021-2027 Resetting education and training for the digital age“. European Commisson. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52020DC0624&from=EN.
Ferrante, P., Büchner, F., Kiesewetter, S., Muyambi, G. C., Uleanya, C., Utterberg Modén, M., & Williams, F. 2023 (under review). In/equalities in Digital Education Policy – Sociotechnical Imaginaries from three World Regions. Learning, Media and Technology.
Fourcade, Marion. 2016. „Ordinalization: Lewis A. Coser Memorial Award for Theoretical Agenda Setting 2014“. Sociological Theory 34 (3): 175–95. https://doi.org/10.1177/0735275116665876.
Helsper, Ellen. 2021. The digital disconnect. Thousand Oaks: SAGE Publications Ltd.
Ljungqvist, Marita, und Anders Sonesson. 2021. „Selling out Education in the Name of Digitalization: A Critical Analysis of Swedish Policy“. Nordic Journal of Studies in Educational Policy, November, 1–14. https://doi.org/10.1080/20020317.2021.2004665.
Macgilchrist, Von Felicitas. 2019. „Digitale Bildungsmedien im Diskurs. Wertesysteme, Wirkkraft und alternative Konzepte“. BPB, Juni, 11.
Svallfors, Stefan, und Anna Tyllström. 2019. „Resilient Privatization: The Puzzling Case of for-Profit Welfare Providers in Sweden“. Socio-Economic Review 17 (3): 745–65. https://doi.org/10.1093/ser/mwy005.


28. Sociologies of Education
Paper

Unpacking the Influence of Classroom and School Factors on Educational Inequality in Luxembourg: A Multilevel Trend Analysis

Sercan Erer1, Andreas Hadjar2,1, Susanne Backes1

1University of Luxembourg, Luxembourg; 2University of Fribourg, Switzerland

Presenting Author: Hadjar, Andreas

This paper attempts to study how school and classroom characteristics shape educational inequalities in Luxembourg. Mainly focusing on unequal distributions of educational resources and opportunities, educational inequality has been in the spotlight of educational sociology. The concept of educational inequality is framed around “systematic variations in several aspects of educational attainment structured by ascribed attributes of students derived from their social group memberships, such as gender, ethnicity, immigrant background and class (axes of inequality)” (Gross et al., 2016, p. 12). Explicitly, a study of variation in educational attainment, according to Jacobs (1996), might embody a disparity in educational trajectories, educational experiences and outcomes (including gained competencies, earned grades and certificates) among students from diverse backgrounds. Unfortunately, educational inequality related to certain axes such as social origin, race, or ethnicity appears to be rather persistent. Thus, it is still a relevant concern in many modern societies.

A prominent study conducted by James S. Coleman and his colleagues (1966) on the examination of schools and student achievement might be considered as a turning-point in the field of educational inequality. The conclusions of the renowned researchers stressed that the primary drivers of student performance are student demographics such as familial resources and race, and also the influence of peer composition in classrooms, rather than the school inputs including school quality and teacher qualifications. These conclusions on the highly underlined influence of a student’s parental resources had a profound impact in the field and shaped the discourse towards general inequality theories on social and cultural factors and on how educational systems reproduce socioeconomic inequalities from the perspectives of Boudon (1974), Bourdieu (1986), and Bourdieu and Passeron (1977). Meanwhile, the conclusions on mostly negligible school effects resulted in a rise of studies in the field of school effectiveness research to unveil effective characteristics of schools on educational achievement with the essential aim of diminishing the achievement gaps of disadvantaged students (Angus, 1993; Burušić et al., 2016; Scheerens, 2016). Consequently, many researchers have hitherto contributed to our modern understanding of how educational inequality perpetuated either by the contributions of individual social, economic, and cultural factors, or by higher level influences such as social compositions in schools and other school inputs regarding many aspects.

Regarding the endurance of educational inequality throughout time and geography, Luxembourg, as one of the most diverse countries in Europe, has its own assets and complications. On the positive sides, its commitment to promoting educational equality, its attempts to provide high-quality school environments, and its society accommodating more than 170 nationalities while operating with three official languages (Luxembourg Ministry of Education, Children and Youth, 2021) are some examples of its unique assets. Yet, as also highlighted in some international and national educational reports (OECD, 2019, 2021; SCRIPT & LUCET, 2018), in this diverse and wealthy country, students from distinct backgrounds still face some common struggles to keep up with their peers from advantageous backgrounds when their gender, language proficiencies, and socioeconomic backgrounds are taken into consideration (Hadjar et al., 2015, 2018). Within the framework of a ministerial project aiming to ensure the continuance of providing equal educational opportunities to students in primary schools of Luxembourg, this study taps on longitudinal patterns of classroom and school impacts on educational inequality. Relying on the results of this study, not only will educational policy makers of the country have grounded scientific evidence to continue to work towards developing policies that can potentially reduce these educational disparities in early stages of Luxembourgish primary schooling, but also the researchers might unveil modern mechanisms to contribute to the field of school effectiveness research and sociology of education.


Methodology, Methods, Research Instruments or Sources Used
This study utilizes a multilevel trend modelling approach with an aim to examine the trend of moderation effects of various classroom compositions, teacher-student ratio and social index on the relationship between different axes of inequality (gender, socioeconomic background, and language) and grade-3 (G3) students’ academic achievement in Luxembourg over 6 consecutive years, while accounting for the nested structure of the dataset. To accomplish this aim, the data used in this study has been merged from different census data sources provided by the Ministry of Education, Children and Youth, Luxembourg Center for Educational Testing, and Luxembourg Institute of Socio-Economic Research, along with standardized test data from the national education monitoring, called ÉpStan, within the framework of a ministerial project.

With its nested structure, the final census dataset consists of 4052-4794 students in G3 within 334-392 classrooms operating under 145-157 schools from 94-99 communes in Luxembourg between 2014 and 2019. Accordingly, Classroom-ID, School-ID and Commune-ID become the clustering variables. Year-ID is utilized to separately conduct multilevel models per year. The outcome variables of interest are the standardized math and German reading comprehension scores in grade 3. While the individual level predictors are the demographics of students such as gender, socioeconomic status (SES), and language spoken at home, the classroom compositions are represented by female percentage, average SES, and percentage of non-Luxembourgish-or-German speakers (nLGs) at home, created by aggregating the individual level demographics onto the classroom level. The school level predictors are school student population and teacher-student (TS) ratio at a given year provided by the ministry. Lastly, social index (Fazekas, 2012) is utilized as a commune-level proxy for the additional monetary compensations provided to communes to tackle educational inequality in Luxembourg.

Using Stata 17, six models per subject-specific score are run with maximum likelihood and available case analysis. The fitness of each model is assessed using associated residual plots and Akaike Information Criterion. Additionally, for each model in the analyses, the intraclass correlations (ICC) are calculated to represent the proportion of variance in the corresponding outcome variable that is explained by the group-level variations. For math scores, they ranged between <1% to 2%, 2.8% to 4.6%, and between 5% to 9%, respectively on the commune, school and classroom levels. For German scores, ICCs ranged between 1% to 3.1%, 3.1% to 6.1%, and between 4.8% to 9.7%, at the commune, school and classroom levels, respectively.

Conclusions, Expected Outcomes or Findings
The main individual effects pointed significant advantages on three axes of inequality consistently throughout the years: gender (males in math and females in German), SES (more affluent students), and language (Luxembourgish or German speaking students). The results from cross-level interactions between individual-level axes of inequalities and classroom, school and commune level variables are intriguing.

On math scores, the significant disadvantage of female students is moderated positively by high TS-ratio schools (2016 and 2017) and by more commune-level monetary compensations (2017). The significant advantage of coming from more affluent families is amplified by high-average-SES classrooms (2015, 2016, 2017, and 2019), but negatively moderated by high TS-ratio schools (2017) and by more commune-level monetary compensations (2018 and 2019). The significant disadvantage of students who are nLGs is reduced by high-percentage-nLGs classrooms (2015) and by more commune-level monetary compensations (2016).

Regarding German reading comprehension scores, the significant disadvantage of male students is moderated positively by more commune-level monetary compensations (2018). The significant advantage of high SES students is amplified also for German scores by high-average-SES classrooms (2015, 2016, 2018, and 2019), but negatively moderated by high TS-ratio schools (2017) and by more commune-level monetary compensations (2019). The significant disadvantage of nLGs students is reduced by high-percentage-nLGs classrooms (2015) and by more commune-level monetary compensations (2014, 2016, and 2019).

Consequently, the multilevel trend analyses unveiled two important aspects: achievement-gap reducing and amplifying mechanisms. More commune-level monetary compensations are predicted to narrow disparities in achievement scores based on all axes of inequality. While high TS-ratio schools reduce gender and SES achievement gaps for math, they diminish only SES achievement gaps for German scores. Moreover, homogenous classroom composition based on language appears to lessen the language achievement gap for both scores. Contrarily, homogenous SES classroom composition appears to amplify student SES achievement gaps for both scores.

References
Angus, L. (1993). The Sociology of School Effectiveness. British Journal of Sociology of Education, 14(3), 333–345. JSTOR.

Boudon, R. (1974). Education, Opportunity and Social Inequality: Changing Prospects in Western Society. Wiley.

Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). Greenwood Press.

Bourdieu, P., & Passeron, J.-C. (1977). Reproduction in education, society and culture (3. pr). Sage.

Burušić, J., Babarović, T., & Velić, M. Š. (2016). School Effectiveness: An Overview of Conceptual, Methodological and Empirical Foundations. In N. Alfirević, J. Burušić, J. Pavičić, & R. Relja (Eds.), School Effectiveness and Educational Management: Towards a South-Eastern Europe Research and Public Policy Agenda (pp. 5–26). Springer International Publishing. https://doi.org/10.1007/978-3-319-29880-1_2

Coleman, J. S., Campbell, E. A., Hobson, C., McPartland, J., Mood, A., Weinfeld, F., & York, R. (1966). Equality of educational opportunity. Washington, DC: U.S. Government Printing.

Fazekas, M. (2012). School Funding Formulas. 74. https://doi.org/10.1787/5k993xw27cd3-en

Gross, C., Meyer, H.-D., & Hadjar, A. (2016). Theorising the impact of education systems on inequalities. In A. Hadjar & C. Gross (Eds.), Education systems and inequalities (1st ed., pp. 11–32). Bristol University Press. https://doi.org/10.2307/j.ctt1t892m0.7

Hadjar, A., Backes, S., & Gysin, S. (2015). School Alienation, Patriarchal Gender-Role Orientations and the Lower Educational Success of Boys. A Mixed-method Study. Masculinities and Social Change, 4, 85–116. https://doi.org/10.4471/MCS.2015.61

Hadjar, A., Krolak-Schwerdt, S., Priem, K., & Glock, S. (Eds.). (2018). Gender and educational achievement. Routledge, Taylor & Francis Group.

Jacobs, J. A. (1996). Gender Inequality and Higher Education. Annual Review of Sociology, 22(1), 153–185. https://doi.org/10.1146/annurev.soc.22.1.153

Luxembourg Ministry of Education, Children and Youth. (2021). The Luxembourg Education System: An overview. https://men.public.lu/dam-assets/catalogue-publications/divers/informations-generales/lu-education-system-UnApercuEN.pdf

OECD. (2019). Education at a Glance 2019: OECD Indicators. OECD. https://doi.org/10.1787/f8d7880d-en

OECD. (2021). Education at a Glance 2021: OECD Indicators. OECD. https://doi.org/10.1787/b35a14e5-en

Scheerens, J. (2016). Educational Effectiveness and Ineffectiveness. In Educational Effectiveness and Ineffectiveness: A Critical Review of the Knowledge Base. Springer Netherlands. https://doi.org/10.1007/978-94-017-7459-8

SCRIPT & LUCET. (2018). Nationaler Bildungsbericht Luxemburg. https://men.public.lu/de/publications/statistiques-etudes/themes-transversaux/18-bildungsbericht.html


 
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