Building the Evidence-Base of Effective Reading Strategies to Use With Deaf English-Language Learners
Bibliographic record
Abstract
Nearly 25% of Deaf and Hard of Hearing (DHH) students come from homes where a language other than English is used and are known as English-Language Learners (ELLs). Evidence-based practices used to teach students who are DHH ELLs are imperative. To build an evidence-base, successful strategies must be examined across multiple researchers, sites, and participants. This research is a replication of an effective reading strategy; teaching vocabulary using repeated preteaching sessions paired with viewing American Sign Language books on DVD. Five participants with severe to profound hearing loss participated in this multiple-baseline design (ABC) across three sets of five vocabulary words study. Results indicated that after three sessions of preteaching and viewing the DVD, the majority of participants signed correctly 90% to 100% of the targeted vocabulary. Maintenance data were collected 1 to 5 weeks following the intervention. Implications for practitioners and researchers are discussed.
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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 | 0.036 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".