Vocabulary enrichment using an E-book with and without kindergarten teacher’s support among LSES children
Bibliographic record
Abstract
We examined an intervention in kindergarten using an e-book for vocabulary enrichment. In programme (a), the children read the e-book with a dictionary and the teacher’s support. In programme (b), the children read the e-book with the dictionary independently. In programme (c), the children read the e-book without a dictionary (control). The participants included 103 children (aged 5–6) from LSES families. They read the e-book in the kindergarten class six times. The children were tested pre, post 1 and post 2, on story focal words at the receptive, explanation and production level. Children who read the e-book with the dictionary and the teacher’s support learned more words than those, who read the e-book with the dictionary independently, and more than the control. Achievements were maintained after one month. Children with an initial low level progressed more than those with a high level. The findings and their implications are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".