Assessing Educator Responsivity in Outdoor Early Childhood Education and Care Settings: Validating the Outdoor Environment Version of the Responsive Interactions for Learning Measure
Bibliographic record
Abstract
Research Findings: Outdoor time is essential in early childhood education, yet quality assessments that are specifically focused on outdoor settings remain limited. Existing indoor measures primarily evaluate environment-level quality while neglecting educator-child interactions and the educators’ central role in children’s central learning. This study evaluates the psychometric properties of the Responsive Interactions for Learning – Outdoor Environment (RIFL-OE), designed for efficient assessment of outdoor interactions. Across 161 educators in 68 outdoor settings, the mean responsivity score was 2.74 on a 5-point scale, which is lower than RIFL scores in indoor classrooms. Confirmatory factor analysis supported a unidimensional model and the measure demonstrated high internal consistency (α = 0.96). Small but significant correlations were found with the Preschool Outdoor Environment Measurement Scale’s Interaction subscale (r = 0.27, p < .001) and total score (r = 0.24, p = .002). Item response theory analyses showed good item discrimination and high information across levels of responsivity. Practice or Policy: RIFL-OE offers an efficient way for practitioners to evaluate educator-child interaction quality in outdoor settings, providing insights to enhance outdoor learning environments and inform policies on outdoor education practices.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".