Allocation of low‐threshold special educational support in lower secondary education

The role of individual and class‐level characteristics

Bibliographic Data

ID21723090
AuthorsEva-Maria Holzer (0000-0001-5197-6407, Institute of Education University of Zurich Zurich Switzerland, corresponding author), Maria Wehren-Müller (0000-0001-7279-7272, Institute of Education University of Zurich Zurich Switzerland), Janina Kraft (0009-0000-8548-7142, Institute of Education University of Zurich Zurich Switzerland), Mireille Tabin (0000-0002-2930-7810, Institute of Education University of Zurich Zurich Switzerland), Elisabeth Moser Opitz (0000-0002-5243-4770, Institute of Education University of Zurich Zurich Switzerland)
Year2026
Volume26
Issue2
Publication date2026-04-01
Open AccessYes
TypeARTICLE
VenueJournal of Research in Special Educational Needs (JOURNAL)
Journal identifiersISSN: 1471-3802 • E-ISSN: 1471-3802
PublisherWiley (PUBLISHER • GB)
DOI10.1111/1471-3802.70096
OpenAlexW7155221138
LanguageEN
References cited44

It is vital that students with learning or behavioural difficulties receive special educational (SE) support from professionals. However, allocation criteria are often unclear. This issue is particularly important in lower secondary schools, where ability tracking is common and external differentiation strategies accommodate achievement differences. This cross‐sectional study examines associations between low‐threshold SE support and individual characteristics, class composition and school type (mixed‐ability versus low‐ability classes) among 941 students (aged 11–18) from 74 classrooms in German‐speaking Switzerland. Logistic multilevel regression analyses revealed that SE support is more often allocated to students with low mathematics achievement, low socioeconomic status (SES), and boys, while migration background and disruptive behaviour are not significant predictors. At the classroom level, lower mean mathematics achievement is associated with a higher likelihood of SE support, although this pattern is partly explained by school type. Importantly, gender and SES remain associated with SE support even when individual mathematics achievement is considered. These findings highlight social disparities in support allocation. Merely identifying students as requiring SE support may lead to unintended consequences, such as lower expectations or stigmatization. Further research is needed to examine how structural and institutional factors determine low‐threshold SE support allocation and shape students' educational trajectories

Academic achievement · Logistic regression · Multilevel model · Multilevel modelling · Social support · Socioeconomic status · Special education · Cognitive and developmental aspects of mathematical skills · Collaborative Teaching and Inclusion · Disability Education and Employment

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