Listening Comprehension and Decoding as Indirect and Direct Predictors of Reading Comprehension in Grades 1, 2, and 3
Bibliographic record
Abstract
Ample research has established that reading comprehension (RC) can be predicted from listening comprehension (LC) and word recognition (WR). An open question is the extent to which each predictor influences the other in their shared prediction of RC. Some studies have examined shared variance, but few have examined how variance is partitioned through direct and indirect paths. Further, how might paths differ across the first years of formal education? We investigated direct and indirect pathways from LC and WR to RC for children in grades 1-3 (n = 263) and evaluated path equivalence across grades. A fully saturated path analysis model was fit to the data using an SEM framework, triangulated with an ordinary least squares model with bootstrapping. Our novel findings are that paths do vary across the primary years and that the indirect effect of LC on RC via WR is significant in grades 1 and 2. Thus, LC development plays a role in the development of WR as both predict RC. As children mature, the direct path from LC to RC becomes stronger, in part because texts require deeper comprehension. These findings suggest broad oral language development uniquely contributes to both word recognition and reading comprehension skills in the primary years.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".