Orthographic and Semantic Learning During Shared Reading: Investigating Relations to Early Word Reading
Bibliographic record
Abstract
PURPOSE: Shared reading provides preschool-age children with the opportunity to learn novel, low-frequency words. Abundant empirical evidence demonstrates that children can learn the meanings of such words during shared reading, referred to as "semantic learning." However, less is known about whether children learn the spellings of words during shared reading, referred to as "orthographic learning," and whether this learning is related to early word reading. The present study tested relations between individual differences in 4- to 6-year-old children's semantic and, critically, orthographic learning during shared reading and their early word reading skill. METHOD: In an adaptation of the self-teaching paradigm, children listened to a storybook about novel inventions referred to with nonword names. Children then completed orthographic and semantic choice tests, as well as standardized measures of early word reading and phonological awareness. RESULTS: Individual differences in orthographic, but not semantic, learning during shared reading were related to early word reading, after controls for age and phonological awareness. CONCLUSIONS: This study provides a novel test of learning during shared reading, helping to specify the relation between orthographic and semantic learning and early word reading skill. These findings hold implications for theoretical perspectives on relations between learning during shared reading and early word reading, as well as implications for educational practice. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25492765.
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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 | 0.004 |
| 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 | 0.001 |
| 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".