Gadgets in the Gymnasium: Physical Educators’ Use of Digital Technologies | Les gadgets au gymnase : l’utilisation des technologies numériques par les enseignants en éducation physique
Bibliographic record
Abstract
This article highlights results from a recent study that investigated Atlantic Canadian physical educators’ adoption and implementation of various digital technologies. Employing a mixed-methods research design (survey participants, n = 206; focus group participants, n = 12), the research intended to provide a clear overview of physical educators’ implementation of digital technologies—as well as an account of the factors that may enable or limit their use. Results suggest that some digital technologies are used more (e.g., audio players, computers) than others (e.g., Dartfish, iTouch). Moreover, a number of external barriers (limitations in time, expertise, resources) and internal barriers (teacher beliefs, established pedagogy) were identified. In light of these results, a number of observations and comments are offered. Results from this research might be of particular interest to those engaged with physical education and technology implementation.Cet article souligne les résultats d’une étude récente qui s’est penchée sur l’adoption et la mise en application de diverses technologies numériques par les moniteurs d’éducation physique du Canada atlantique. À l’aide d’un modèle de recherche faisant appel à des méthodes mixtes (participants au sondage, n = 206; participants au groupe de discussion, n = 12), l’étude entendait fournir un survol limpide de la mise en œuvre des technologies numériques par les enseignants en éducation physique, ainsi qu’un compte-rendu des facteurs qui peuvent permettre ou limiter cet usage. Les résultats suggèrent que certaines technologies numériques sont plus utilisées (p. ex. lecteurs audio et ordinateurs) que d’autres (p. ex. Dartfish, iTouch). De plus, un certain nombre d’obstacles externes (des limites relatives au temps, à l’expertise, aux ressources) et internes (croyances des enseignants, pédagogie établie) ont été repérés. À la lumière de ces résultats, nous offrons certaines observations et des commentaires. Les résultats de ces recherches peuvent être intéressants pour les personnes qui s’occupent de l’éducation physique et de la mise en application des technologies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".