Assessing Educator Responsivity in Outdoor Early Childhood Education and Care Settings: Validating the Outdoor Environment Version of the Responsive Interactions for Learning Measure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it