Identity Texts and Literacy Development among Preschool English Language Learners: Enhancing Learning Opportunities for Children at Risk for Learning Disabilities
Bibliographic record
Abstract
There is little research on English language learners (ELLs) in relation to learning disability (LD) assessment and identification. More important, there is a scarcity of research on models and strategies that enhance learning opportunities and outcomes for ELLs prior to an LD diagnosis. We describe in this article an innovative language intervention program involving the creation of bilingual, student self-authored identity texts. Called the Early Authors Program (EAP), the intervention stands as an example of how spaces and opportunities for literacy development among young ELLs can be created in a classroom instructional environment. The EAP, which reached 800 families, was evaluated using a combination of methods and instruments. The goal of the evaluation component was to collect data spanning one year from 325 randomly selected participating children in both control and experimental groups. Among its several beneficial outcomes, the EAP had demonstrably positive effects on children's language scores and appears to have strengthened their identities and fostered their self-esteem. Because a proportion of these students would be at risk for LD, we propose the implementation of programs of this type generally for ELL children, and especially for those considered likely to have future school-related difficulties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".