Shifting perspectives: developing and integrating educators’ notions of play and early math learning in kindergarten
Bibliographic record
Abstract
Growing evidence promotes the suitability of play-based learning to support young children’s developmental and academic learning. Educators’ differing perspectives of play and learning, however, lead to differing implementations of play in classrooms settings that either dichotomise or integrate the constructs of play and academic learning. As contemporary notions of play advocate for the integration of play and academic learning, this current study sought to understand the ways in which kindergarten educators were successfully shifting their perspectives and practices to align with more current views of play-based learning in kindergarten education. Through thematic analysis of interview data, this study identified how educators progressed through four stages of evolution in their perspectives of both play and early math learning in kindergarten. This professional evolution led to an integrated perspective of how various approaches to play can support young children’s math learning in practice. Discussion of these findings will highlight the key factors that helped educators to make a positive change in their professional practice through shifting their perspectives of play and math learning in kindergarten.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".