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Phonological working memory and non-verbal complex working memory as predictors of future English outcomes in young ELLs

Domaine:

education

Type de record:

paper
Créateur:
Mic
Éditeur:
SAG
Hôte:
Aim of the study: The aim of this longitudinal study was twofold: first, to determine whether the relationship between working memory measures and language performance in young English language learners (ELL) remains constant over the year. The second aim was to determine if performance on working memory tasks predicts future performance on language measures. Methodology: The participants were 27 ELLs between the ages of 5 and 6 years who were in their first year of formal schooling and attended the same mid-low socioeconomic status school in South Africa. The participants were tested three times throughout the year on tasks of working memory and an English assessment battery. Data analysis and results: Mixed effects models and multiple linear regression were used to address the aims of the study. The first aim of the study showed that there are significant correlations between all working memory measures and all language measures in varying strengths across the year. The second research aim further elaborated on this by showing that both phonological working memory and non-verbal complex working memory are implicated in the acquisition of syntax, semantics and pragmatics at different points throughout the year. Implications and originality: Language acquisition in ELLs is not a stand-alone process and working memory measures may be able to predict future language outcomes. This could indicate that working memory measures may be used as an indicator for who may need language intervention, at a time when the ELL only has limited English proficiency and limited English exposure. This research is the first of its kind to originate from Africa, with a sample from low socioeconomic, culturally and linguistically diverse circumstances who are exposed to English consistently for the first time and are tested with working memory tasks with less strong language components.

Visit

doi.org

Licenses

https://journals.sagepub.com/page/policies/text-and-data-mining-license

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