Improving English reading fluency and comprehension for children with reading fluency disabilities
Bibliographic record
Abstract
In the English language, students who read words accurately but have impairments in reading fluency are under-studied. The associated difficulties they have with comprehending text make it particularly important to delineate effective interventions for these students. Counter to suggestions that these readers need interventions focused on text reading, we examined the effects of a decoding-focused intervention. The intervention targeted decoding-related skills, including speeded training on sublexical spelling patterns. We examined the efficacy of this program for students with fluency-defined disabilities, and compared gains to those for students with accuracy-defined disabilities. In the initial phase of the program, readers with fluency-defined disabilities made greater gains in fluency, while readers with accuracy-defined disabilities made larger gains in word reading accuracy. The mean fluency score for readers with fluency-defined disabilities came within the average range across the intervention, as did reading comprehension for both groups. Readers' mastery on speeded learning of sublexical spelling patterns predicted unique variance in fluency outcomes, beyond variance accounted for by pre-test fluency and word reading accuracy. The results support intervention approaches focused on decoding-related skills for students who have fluency-defined disabilities and are consistent with theories of reading fluency that identify a role for automaticity with sublexical spelling patterns.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".