Testing mechanisms underlying children’s reading development: The power of learning lexical representations.
Bibliographic record
Abstract
Prominent theories of reading development have separately emphasized the relevance of children's skill in learning (Share, 2008) and lexical representations (Perfetti & Hart, 2002). Integrating these ideas, we examined whether skill in learning lexical representations is a mechanism that might explain children's reading development. To do so we conducted a longitudinal study, following 139 children from Grades 3 to 5. In Grade 3, children completed measures of word reading and reading comprehension and again at Grade 5. In Grade 4, children read short stories containing novel words; they were later tested on their memory for the spellings and meanings of these new words, capturing orthographic and semantic learning, respectively. Using multiple-mediation path analysis, we tested whether children's skill in learning orthographic and semantic dimensions of new words was a mediator of individual differences in each of word reading and reading comprehension. In models controlling for nonverbal ability, working memory, vocabulary, and phonological awareness, we found two clear effects: individual differences in orthographic learning at Grade 4 mediated the gains that children made in word reading between Grades 3 and 5 and individual differences in semantic learning at Grade 4 mediated gains in reading comprehension over the same time period. These findings suggest that children's ability to learn lexical representations is a mechanism in reading development, with orthographic effects on word reading and semantic effects on reading comprehension. These findings show the power and the specificity of children's capacity to learn in determining their progress in learning to read. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".