Teaching Word Recognition to Children with Intellectual Disabilities
Bibliographic record
Abstract
Learning to read affords individuals with intellectual disabilities (ID) a means to function in a literate society. However, one of the most overwhelming challenges for children with ID to accomplish is learning how to read independently. In this study a three-step decoding strategy was used with a constant time delay procedure to teach word reading to children with ID using a phonics-based curriculum. A non-concurrent multiple baseline design with two intervention phases was used to examine the percentage of letter-sounds correctly decoded and the percentage of words read correctly. The findings indicated that all the children learned to read words using the three-step decoding strategy and constant time delay procedure. It was also noted that across all children letter-sound decoding accuracy outpaced word reading accuracy. Although each child made gains in reading words, these gains were not sufficient to infer generalization. These results suggest that the decoding strategy and time delay procedure may be effective at instructing children with ID who are having a difficult time blending sounds together to read word, but additional supports are warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".