The comprehension skills of children learning English as an additional language
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".