Coaching as a Strategy for Improving Early Learning Program Quality
Bibliographic record
Abstract
As professionals and governments increasingly recognize the role early learning programs play in the young child’s learning and development, agencies and governments alike are trying to determine effective strategies for enhancing program quality. While coaching has gained popularity as a method to improve program quality, there has been little research on coaching within the licensed early learning and care sector in Canada. The current study utilized a mixed methods approach, combining both quantitative and qualitative methods, to better understand how a quality improvement process and the use of coaches support changes to program quality. The quality improvement process provided a structure for educators to examine practices, set goals, and implement changes while the coaches provided support and guidance to educators engaged in the process. The main quantitative method utilized the Early Childhood Education Rating Scale-Revised (ECERS-R) to evaluate program quality before and after the implementation of the process. While the results were not statistically significant, there were some indicators that the process and the use of coaches might support program quality improvements. The analysis of qualitative data (from survey questions and interviews) resulted in themes regarding both the quality improvement process and the value of coaches. The strongest overarching themes regarding the implementation of the process were: 1) Need for Time and 2) Importance of Reflective Practice. An additional theme was - Process Results in Positive Outcomes. The overarching themes regarding coaches were: 1) Coaches Provide Resources, 2) Coaches Provide Clarity/Insight, 3) Coaches are Mediators/Facilitators, and 4) Coaches Help Educators Complete the Process. The limitations of the study (e.g., small sample size, use of volunteers) restricted generalizations, but the results did highlight the need for more research and provided some implications for agencies and governments looking to improve program quality.
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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 | 0.027 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".