Read-Aloud Technique To Enhance Pre-School Children’sVocabulary In A Rural School In Malaysia
Bibliographic record
Abstract
Vocabulary knowledge and acquisition plays an important role in learning a second language as well as developing children’s literacy skills. The effectiveness of the read-aloud technique to increase children’s vocabulary knowledge and construction of meaning has been widely studied. Teachers need to employ effective instructional strategies to foster growth in vocabulary learning among pre-school children during the read-aloud sessions. Given this, this study has identified the vocabulary strategies that a teacher employed during her read-aloud sessions with her pre-school children in a rural school in Malaysia. A pre-school teacher from a pre-school situated in a rural area participated in this study. Qualitative research methods were used whereby primary data was obtained through observations and field notes while secondary data was obtained through interviews with teachers. Findings revealed that the pre-school teacher utilized four vocabulary strategies proposed by Beck, McKeown and Kucan (2002) as well as the children’s L1 which is Malay language to explain meanings of words during storybook read-aloud sessions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".