Digital technology in physical education global perspectives
Bibliographic record
Abstract
The rapid development of digital technologies has opened up new possibilities for how Physical Education is taught. This book offers a comprehensive, practice-oriented and critical exploration of the actual and potential applications of digital technologies in PE. It considers the opportunities that are offered by new technologies and how they may be best implemented to enhance the learning process. Including contributions from the US, UK, Europe, Canada and New Zealand, this international collection reflects on how digital innovations are shaping PE pedagogy in theory and practice across the globe. Its chapters identify core pedagogical principles – rather than simply discussing passing digital fads – and offer practical narratives, case studies and reflections on how PE practitioners can introduce technology into teaching and learning through the use of social media, video gaming, virtual reality simulation, iPads and Wiki platforms. Digital Technology in Physical Education: Global Perspectives is a valuable resource for students, researchers and practitioners of PE looking to integrate digital technology into their work in a way that does justice to the complexity of teaching and learning.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".