Bibliographic record
Abstract
How do we encourage teachers to adopt Teaching Games for Understanding (TGfU) so that it becomes part of mainstream practice in physical education and community-based sports programmes worldwide? Why do some teachers adopt a TGfU instructional model and others stick to a technique-based approach? What happens to PETE students when they attempt to take innovative ideas out into the field? Can ‘old dogs’ learn new tricks? Can ‘puppies’ do things differently from their mentors out in the school system? Ryan and Cooper delineated five stages typical of the teaching career: (1) fantasy; (2) euphoria; (3) survival; (4) apprenticeship; and (5) rediscovering the dream. This article explores the journeys of both beginning and experienced teachers using these stages and suggests some ways in which TGfU can become part of their exploration. Moving from the comfort zone into unfamiliar areas can be a challenge, but paradoxically, the very discomfort caused by disconnect between avowed principle and actual practice, educational philosophy and teaching methodology, can be a wonderful incentive. The essential ingredient for change is a core belief in innovation rather than previous practice or experience. This taps into the passion and idealism that drove us to join the profession in the first place, to want to become the best that we can be as educators, and dare to hope for the same for our students. Teacher responses to TGfU have great implications for curriculum design and suggest the need for an integrated approach, which provides structured, careful support for teachers through the process of change. This article will suggest ways to promote and integrate TGfU into the repertoire of experienced teachers, and to give students the opportunity to explore the approach. These include the establishment of a worldwide TGfU task force, an emphasis on action research and student exposure to constructivist learning theory through majors' clubs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| 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".