Teacher deliberation within the context of Singaporean curricular change: pre‐ and in‐service PE teachers’ perceptions of outdoor education
Bibliographic record
Abstract
ABSTRACT The study featured in this paper investigates pre‐ and in‐service Physical Education (PE) teachers' diverse views of OE (Outdoor Education) against the backdrop of numerous curricular changes underpinning Singaporean education. We highlight the potential challenges Singaporean schools and teachers may face in implementing the newly formalized OE curriculum. Findings revealed the teachers' prioritization of high elements and adventure activities within residential camps. Then, the teachers linked these ‘risky’ and ‘unfamiliar’ activities with transferrable learning outcomes, mostly in line with students' personal and social development. The more experienced teachers in the cohort tended to advocate that OE could be situated within local school and community environs. Yet, this envisioning of OE within a place‐based learning model reflected limited understanding of this concept. We conclude by suggesting that curriculum designers and teacher educators should draw upon pre‐ and in‐service PE teachers' perceptions of OE. It is imperative that these teachers' key concerns and beliefs, which currently structure their work in this recently formalized subject area, are used to contextualize the extensive curricular change and professional learning endeavors that are being implemented by the education ministry.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".