Sign it, say it, read it: the effectiveness of American sign language as a supplement to reading instruction for children with Down syndrome
Bibliographic record
Abstract
"Sign it, Say it, Read it" was a 16 session study designed to isolate and examine the effect of using sign language within a comprehensive reading program for students with intellectual and developmental disabilities. A group of 19 students were divided between a treatment and a control group. The treatment group received a comprehensive reading intervention augmented with explicit sign language instruction. The control group received the same comprehensive reading program, but without the sign instruction. Initial and final assessments were conducted of the entire group using a mix of standardized tests and informal inventories. For 16 sessions, a teacher at the Down Syndrome Research Foundation delivered a reading program specific to this population of students. In conjunction, two school reinforcement sessions occurred each week for the duration of the study. The pre and post-performance measure scores were analyzed using repeated measures analysis of variance, (ANOVA) and t-tests for within subjects and between groups. Significant results were found for within subject ANOVA tests. Large effect sizes were found for the treatment group when comparing between group paired t-tests. The findings suggest that this intervention is effective for students with ID/DD. It also appears that sign language augmentation favourably affects language and literacy outcomes. Follow up investigation using a larger sample size for a longer period of time is recommended.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".