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Cited by 260

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Working Memory, but Not IQ, Predicts Subsequent Learning in Children with Learning Difficulties

Open access
Alloway, Tracy Packiam·2009·European Journal of Psychological Assessment

Abstract

The purpose of the present study was to compare the predictive power of working memory and IQ in children identified as having learning difficulties. The term “working memory” refers to the capacity to store and manipulate information in mind for brief periods of time. Working-memory capacity is strongly related to learning abilities and academic progress, predicting current and subsequent scholastic attainment of children across the school years in both literacy and numeracy. Children aged between 7 and 11 years were tested at Time 1 on measures of working memory, IQ, and learning. They were then retested 2 years later on the learning measures. The findings indicated that working-memory capacity and domain-specific knowledge at Time 1, but not IQ, were significant predictors of learning at Time 2. The implications for screening and intervention are discussed.

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Metadata

Authors
Alloway, T. P.
Venue
European Journal of Psychological Assessment
Published
2009-01
Volume
25
Issue
2
Pages
92-98
ISSN
  • 1015-5759 (ISSN-L)
  • 2151-2426
OpenAlex
W2146857375
Cited by
260
References
11
FWCI
23.30
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Working Memory, but Not IQ, Predicts Subsequent Learning in Children with Learning Difficulties. https://app.cochat.ai/papers/10.1027/1015-5759.25.2.92
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Alloway, T. P. (2009). Working Memory, but Not IQ, Predicts Subsequent Learning in Children with Learning Difficulties. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759.25.2.92

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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.