An effective method for building meaning vocabulary in primary grades.
Bibliographic record
Abstract
Teaching vocabulary to primary grade children is essential. Previous studies of teaching vocabulary (word meanings) using story books in the primary grades reported gains of 20%-25% of word meanings taught. The present studies concern possible influences on word meaning acquisition during instruction (Study 1) and increasing the percentage and number of word meanings acquired (Study 2). Both studies were conducted in a working-class school with approximately 50% English-language learners. The regular classroom teachers worked with their whole classes in these studies. In Study 1, average gains of 12% of word meanings were obtained using repeated reading. Adding word explanations added a 10% gain for a total gain of 22%. Pretesting had no effect on gains. In Study 2, results showed learning of 41% of word meanings taught. At this rate of learning word meanings taught, it would be possible for children to learn 400 word meanings a year if 1,000 word meanings were taught. The feasibility of teaching vocabulary to primary grade children is 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".