Evidence for complementary effects of code- and knowledge-focused reading instruction
Bibliographic record
Abstract
There is growing recognition of the need to end the debate regarding reading instruction in favor of an approach that provides a solid foundation in phonics and other underlying language skills to become expert readers. We advance this agenda by providing evidence of specific effects of instruction focused primarily on the written code or on developing knowledge. In a grade 1 program evaluation study, an inclusive and comprehensive program with a greater code-based focus called Reading for All (RfA) was compared to a knowledge-focused program involving Dialogic Reading. Phonological awareness, letter word recognition, nonsense word decoding, listening comprehension, reading comprehension, written expression and vocabulary were measured at the beginning and end of the school year, and one year after in one school only. Results revealed improvements in all measures except listening comprehension and vocabulary for the RfA program at the end of the first school year. These gains were maintained for all measures one year later with the exception of an improvement in written expression. The Dialogic Reading group was associated with a specific improvement in vocabulary in schools from lower socioeconomic contexts. Higher scores were observed for RfA than Dialogic Reading groups at the end of the first year on nonsense word decoding, phonological awareness and written expression, with the differences in the latter two remaining significant one year later. The results provide evidence of the need for interventions to support both word recognition and linguistic comprehension to better reading comprehension.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".