Feasibility and initial psychometric properties of the Observe, Reflect, Improve Children’s Learning Tool (ORICL) for Early Childhood Services: A tool for building capacity in infant and toddler educators
Bibliographic record
Abstract
Child observation is a critical component of quality pedagogical practice in early childhood education and care (ECEC). Yet there are very few tools that support educators to systematically undertake observations to better understand the individual experiences of very young children within ECEC services. The ORICL (Observe, Reflect, Improve Children’s Learning) tool was co-designed by ECEC experts, service providers and educators to be used for this purpose by educators working with children aged under three years. It is a unique 117-item educator report across five domains of learning and well-being that rates the experiences of individual children, and the responses of educators and peers to the child’s initiatives, actions and communications. This paper describes the first feasibility study of ORICL in 12 ECEC services across Australia with a focus on the quantitative child data collected, and early psychometric properties of the tool. ORICL records were provided by 21 educators for a total of 66 children. Findings suggest that the ORICL items can be readily observed and rated by educators for children aged under three years, the rating scale is appropriate, and there is early evidence to support the domain structure of the tool. Further research on the ways such a tool can provide useful data for both educators and researchers, and stimulate enhanced practice in infant-toddler ECEC, is warranted.
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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 | 0.051 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".