Facilitating Emergent Literacy: Efficacy of a Model That Partners Speech-Language Pathologists and Educators
Bibliographic record
Abstract
PURPOSE: This study examined the efficacy of a professional development program for early childhood educators that facilitated emergent literacy skills in preschoolers. The program, led by a speech-language pathologist, focused on teaching alphabet knowledge, print concepts, sound awareness, and decontextualized oral language within naturally occurring classroom interactions. METHOD: Twenty educators were randomly assigned to experimental and control groups. Educators each recruited 3 to 4 children from their classrooms to participate. The experimental group participated in 18 hr of group training and 3 individual coaching sessions with a speech-language pathologist. The effects of intervention were examined in 30 min of videotaped interaction, including storybook reading and a post-story writing activity. RESULTS: At posttest, educators in the experimental group used a higher rate of utterances that included print/sound references and decontextualized language than the control group. Similarly, the children in the experimental group used a significantly higher rate of utterances that included print/sound references and decontextualized language compared to the control group. CONCLUSION: These findings suggest that professional development provided by a speech-language pathologist can yield short-term changes in the facilitation of emergent literacy skills in early childhood settings. Future research is needed to determine the impact of this program on the children's long-term development of conventional literacy skills.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".