Getting at How: Testing Mediating Factors in the Relation Between Morphological Awareness and Reading Comprehension in Grade 1
Bibliographic record
Abstract
ABSTRACT The relation of morphological awareness with reading comprehension is well established. For this advance to inform instruction, the push is now on to understand how morphological awareness is related to reading comprehension. We address this question here by examining potential mechanisms. We do so with children in Grade 1, a time at which it is widely debated as to whether to include morphology instruction. We examine three potential mediating factors through which morphological awareness may contribute to reading comprehension: word reading, vocabulary, and morphological analysis, or the ability to use morphemes to understand morphologically complex words. A total of 336 English‐speaking Grade 1 children participated and completed control measures of phonological awareness, nonverbal ability, socioeconomic status, working memory, and age. Initial confirmatory factor analyses showed the separability of morphological awareness, morphological analysis, and vocabulary. Multivariate path analyses revealed two indirect pathways from morphological awareness to reading comprehension: through word reading and through morphological analysis. There were no indirect effects via vocabulary, nor were there direct effects from morphological awareness to reading comprehension beyond the other variables. These findings identify two potential mechanisms by which morphological awareness connects with early reading comprehension. These findings will inform theories of reading development and may influence the development of effective early reading instruction.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".