Can a Brief Professional Development Improve Early Childhood Educators’ Responsivity and Interaction Quality in Child Care Centers? A Cluster Randomized Controlled Trial
Bibliographic record
Abstract
High-quality early childhood education and care (ECEC) – particularly care defined by highly responsive interactions between educators and children – has the potential to have lasting positive impacts on children’s development. While there is variability in the level of quality among early education and care settings, professional development for early childhood educators has been shown to be an effective means to improve both ECEC quality and child outcomes. As many professional development programs are time and resource intensive, we sought out to test the efficacy of a brief (5 hr) professional development program that included a workshop, individual coaching, video feedback and text messaging. Research Findings: Results of a cluster randomized controlled trial with 93 educators indicated that the program improved educators’ responsivity three-months after intervention (d = 0.60, p = .035), but not classroom-wide levels of emotional support or instructional quality. Trend analysis revealed the greatest improvements occurred after the workshop and first coaching session and leveled off over time. Practice or Policy: Preliminary evidence suggests brief professional development programs may improve interaction quality with effect sizes comparable to those of longer programs. Well-powered studies using multiple arms or sequential randomization will help optimize the efficiency and effectiveness of professional development.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".