Early childhood educator training: The value of educating educators on movement, play, and physical literacy development – A three country case study
Bibliographic record
Abstract
A child's early movement and active play experiences influence their attitudes towards physical activity throughout their childhood and into adulthood (Blair, 1992). Yet, it has been suggested that early childhood educators (ECEs) may not recognize the importance of, or give enough attention to, movement skills and physical development opportunities for young children (Clark, 2014; Whitehead, 2010). The education, or lack thereof, that ECEs receive could be an important factor. International comparisons of overall ECE preparation and training have demonstrated that some countries' ECEs are more highly educated than others (Howells and Sääkslahti, 2019). Using a case study approach, this paper conducts an analysis and comparison of three countries to examine the value and role of physical activity/movement education for ECEs to enable them to support physical literacy development in early childhood educational settings. In addition, lessons learned from creating such educational opportunities in the context of their various locations (Manitoba in Canada, Kent in England, and Escambia County, FL in U.S.A.) are discussed. A purposeful sample was used as these countries have relatively low levels of educational requirements for ECEs, yet children start attending early years’ education from the earliest life points (Howells & Sääkslahti, 2019).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".