Effects of Explicit and Non-explicit Versions of an Early Intervention Program Incorporating Indigenous Culture into Kindergarten Literacy Instruction
Bibliographic record
Abstract
Abstract Low literacy is a challenge facing Indigenous communities across North America and is an identified barrier to school success. Early literacy intervention is an important target to reduce the discrepancies in literacy outcomes. The Moe the Mouse® Speech and Language Development Program (Gardner & Chesterman, 2006) is a cultural curriculum created to improve the early language skills of students aged three to five, but its effectiveness in improving early literacy skills has yet to be assessed. An enhanced Moe the Mouse® program, created by the first author, integrates explicit instruction in phonological awareness into the Moe the Mouse® program. The purpose of the current study was to evaluate the effectiveness of the two programs. One hundred Kindergarten students at six elementary schools participated in this study. A quasiexperimental pre-post cluster design with three conditions was used. Before and after the program, phonological awareness skills of the students were assessed. Across the intervention, statistically significant differences were found in relation to phonological skills. After the intervention, a statistically significantly smaller proportion of students from the enhanced Moe the Mouse® program fell in the “At Risk” category for later reading difficulties when compared to the other conditions. Additionally, both programs were rated by teachers as socially valid and culturally responsive.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".