Using Yoga as a Personalized and Emergent Model for Early Childhood Educators
Bibliographic record
Abstract
Personalized and emergent instruction is an effective model for Early Childhood Educators (ECEs) and students to consider and use in their own teaching. This workshop utilizes yoga as an experiential and emergent model with ECEs to be reproduced for contextualized and meaningful learning in the early childhood environment. The workshop highlights how children’s literacy and personal development can be enhanced by yoga or other personalized sessions with one-time or multiple-session use.\nAs teachers, it can be advantageous when we bring personal interests into the teaching context. These personal interests can be seen as strengths, hooks, or ways of engaging others in our lessons as well as making learning contextualized and meaningful. We cannot be interested, knowledgeable and ‘strong’ in every area, but that should not prevent us from bringing in slices of interesting tidbits from many domains to personalize teaching with adults or children. This workshop utilizes yoga in the early child-centered education context to demonstrate how children and early childhood educators can experience literacy and personal development benefits either from a one–time exposure or from repetitive use of personalized sessions.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".