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Cited by 2,526

The DOI matches its Crossref record.

International Journal of Special Education

Open access
·2022

Abstract

While there are hundreds of studies reported for general education, few researchers have focused on the impact of class size on the academic achievement of students with special needs. Despite escalating special education costs and increasing student needs, policies governing special education remain inconsistent. We examine the effect of class size reduction on special education students. Two issues were explored: (a) appropriate class size and caseload as they influence special education student academic achievement and (b) the effect of class size on special education teacher attrition rate and teaching methods. The implications of these issues for policy makers are also discussed. Findings indicate 1) each state has different rules on class size and caseload for special education, 2) the students demonstrating the most profound needs remain largely unaffected by class size reduction, and 3) that attrition rate is affected more by quality of teacher preparation than it is by class size. Teachers felt inadequately prepared for inclusion and indicated that their primary need was for more specific inclusion training. We identified no single best way to determine appropriate class and group size for special instructional programs and services; however, the existence of wellqualified teachers proved an important factor in increasing student achievement.

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Metadata

Published
2022-07-25
OpenAlex
W4205630653
Cited by
2,526
References
0
Type
paratext
Language
en
Open access
green

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CoChat. (2026). Citation check: International Journal of Special Education. https://app.cochat.ai/papers/10.52783/ijse
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International Journal of Special Education. (2022). https://doi.org/10.52783/ijse

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.