Learning Word Meanings from Teachers’ Repeated Story Read-Aloud in EFL Primary Classrooms
Bibliographic record
Abstract
This study used a quasi-experimental design to determine the effects of teachers’ story read-aloud on EFL elementary school students’ word learning outcomes. It specifically examined whether the word learning was enhanced by teachers’ repeated story read-aloud and word-meaning explanations and further determined whether the learning outcomes were related to children’s English proficiency. Two native English-speaking teachers read a story to their fourth-grade classes four times. The results showed that increasing frequency of story read-aloud yielded greater word-learning gains across time. The EFL children, on average, learned approximately half of the target words by the third read-aloud. While both high- and low-proficiency groups showed significant vocabulary gains with the frequency of teachers’ read-aloud, the high-proficiency children consistently outperformed their low-proficiency peers, especially on the L1 meaning-matching vocabulary test. The overall findings were quite encouraging and showed empirical evidence that teachers’ repeated story read-aloud can be an effective way to facilitate elementary school children’s word learning in a context where English is a foreign language.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".