What is important? How one early childhood teacher prioritised meaningful experiences for children in physical education
Bibliographic record
Abstract
Attention to the meaningfulness of children’s physical education experiences can promote rich, life-enhancing engagement with movement. While there has been recent interest in prioritising meaningfulness in physical education, this is not matched by vivid descriptions or empirical evidence of how to teach for meaningfulness, particularly in early childhood settings. Adopting a single exploratory qualitative case study design, the purpose of this research was to share how one teacher (Zack) prioritised meaningfulness in one unit of physical education with children aged 3-4 years. Data sources included lesson plans (n = 6), teacher reflections (n = 8), teacher narrative (n = 1), teacher artifacts (including Tweets and photographs (n = 95)) and student interviews (n = 4). Inductive analysis (Thomas, 2006) lead to the identification of pedagogies that were used to prioritise meaningfulness, several of which are representative of Zack’s pedagogical approach. Specifically, stories provided a pedagogical frame for children to move with purpose. Zack scaffolded activities upon the stories to help children make choices and decisions about their participation and provided them with a language to reflect on those experiences. These findings exemplify what a pedagogy of meaningfulness might include in early childhood settings and provides important direction for future implementation.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".