The effects of age and sublexical automaticity on reading outcomes for students with reading disabilities
Bibliographic record
Abstract
For students with reading disabilities, reading fluency has proven difficult to remediate. The current study examined age‐related effects on measures of word and text‐reading outcomes, within the context of a phonologically based remedial reading program. The contribution of speeded‐reading of sublexical sound–spelling patterns to fluency outcomes was also examined. The youngest group of participants showed better outcomes on measures of word and pseudoword reading. All age groups made significant and meaningful improvements on measures of reading fluency and reading comprehension. Participants' mastery of speeded, sublexical sound–spelling reading contributed variance to fluency outcomes beyond pre‐intervention fluency scores. Practice with sublexical spelling patterns may be one important component of programs directed at remediating accuracy and fluency deficits for students with reading disabilities. What is already known about this topic Reading fluency has proven difficult to remediate for students with reading disabilities. Training with sublexical sound–spelling patterns has increased recognition of the trained patterns, but transfer has been limited. Young children with reading difficulties appear to have an advantage at closing the reading achievement gap; however, there are some inconsistencies in the literature. What this paper adds Automaticity with sublexical patterns made a unique contribution to fluency outcomes in this sample of students with reading disabilities. In the context of the reading program examined, all age groups made significant and meaningful standard score gains on reading fluency. Young children did not score higher than the two older groups on measures of oral reading fluency or reading comprehension; bringing into question conclusions drawn from prevention versus intervention studies. Implications for theory and practice Findings lend support to models of reading acquisition that emphasize multilayered, sublexical spelling–sound knowledge as important to reading fluency, beyond that of sight‐word reading efficiency. Including speeded practice of a broad range of sublexical sound–spelling patterns and training these to mastery deserves further study as one potential approach to improving fluency interventions for students with reading disabilities. We suggest that this sublexical training may mimic reading practice in terms of building orthographic representations that support fluent reading.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".