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Record W1981730222 · doi:10.1348/000709909x422530

The comprehension skills of children learning English as an additional language

2009· article· en· W1981730222 on OpenAlexaff
Kelly Burgoyne, Jo Kelly, H. Whiteley, A. Spooner

Bibliographic record

VenueBritish Journal of Educational Psychology · 2009
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsDown Syndrome Research Foundation
Fundersnot available
KeywordsPsychologyReading comprehensionVocabularyComprehensionCurriculumLiteracyReading (process)Active listeningTest (biology)Mathematics educationDevelopmental psychologyLinguisticsPedagogyCommunication

Abstract

fetched live from OpenAlex

BACKGROUND: Data from national test results suggests that children who are learning English as an additional language (EAL) experience relatively lower levels of educational attainment in comparison to their monolingual, English-speaking peers. AIMS: The relative underachievement of children who are learning EAL demands that the literacy needs of this group are identified. To this end, this study aimed to explore the reading- and comprehension-related skills of a group of EAL learners. SAMPLE: Data are reported from 92 Year 3 pupils, of whom 46 children are learning EAL. METHOD: Children completed standardized measures of reading accuracy and comprehension, listening comprehension, and receptive and expressive vocabulary. RESULTS: Results indicate that many EAL learners experience difficulties in understanding written and spoken text. These comprehension difficulties are not related to decoding problems but are related to significantly lower levels of vocabulary knowledge experienced by this group. CONCLUSIONS: Many EAL learners experience significantly lower levels of English vocabulary knowledge which has a significant impact on their ability to understand written and spoken text. Greater emphasis on language development is therefore needed in the school curriculum to attempt to address the limited language skills of children learning EAL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.325
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations63
Published2009
Admission routes1
Has abstractyes

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