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Cited by 210

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

High Stakes Testing: Issues, Implications, and Remedies for English Language Learners

Solórzano, Ronald W.·2008·Review of Educational Research

Abstract

This article discusses the issues and implications of high stakes tests on English language learners (ELLs). As ELLs are being included in all high stakes assessments tied to accountability efforts (e.g., No Child Left Behind), it is crucial that issues related to the tests be critically evaluated relative to their use. In this case, academic achievement tests are analyzed relative to their norming samples and validity to determine their usefulness to ELLs. Also, commonly used language proficiency tests are examined relative to definitions of proficiency, technical quality, alignment with criteria for language classification and reclassification, and their academic predictive validity. Based on the synthesis of the literature, the author concludes that high stakes tests as currently constructed are inappropriate for ELLs, and most disturbing is their continued use for high stakes decisions that have adverse consequences. The author provides recommendations for addressing the issues related to high stakes tests and ELLs.

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Metadata

Authors
Solórzano, R. W.
Venue
Review of Educational Research
Published
2008-06
Volume
78
Issue
2
Pages
260-329
ISSN
  • 0034-6543 (ISSN-L)
  • 1935-1046
OpenAlex
W2159131964
Cited by
210
References
2
FWCI
65.87
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: High Stakes Testing: Issues, Implications, and Remedies for English Language Learners. https://app.cochat.ai/papers/10.3102/0034654308317845
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Solórzano, R. W. (2008). High Stakes Testing: Issues, Implications, and Remedies for English Language Learners. Review of Educational Research. https://doi.org/10.3102/0034654308317845

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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.