Bibliographic record
Abstract
Teacher effectiveness has a positive influence on student achievement and learning (Heck, 2009). Consequently, there is considerable value in better understanding the potential skills that can help make teachers more effective. For physical educators, it has been suggested that strategic use of mental imagery could be associated with teacher effectiveness (Hall, 2012) but minimal research has examined this with the general PE teacher population. This study investigated PE teacher's perceptions and use of imagery as part of teaching. Furthermore, this study sought to establish possible associations between PE teacher characteristics (e.g., grade level taught; years experience; gender; teacher education) and teacher's use of imagery. A total of 150 Canadian PE teachers (76 male, 70 female, 4 undeclared; M years-experience = 12.9) completed the Imagery Use by PE Teachers Survey. The survey focused on PE teacher's perceived frequency of imagery use based around common teaching behaviours (e.g., planning; assessment; skill development). Results demonstrated that the majority of teachers (68.5%) believed imagery was extremely important as an aid for performing various teaching behaviours, with the largest number of teachers (n=125) indicating they use imagery to help teach specific physical skills, and also for personal reflection (n=108). Yet, 78.5% of teachers reported most commonly using imagery prior to delivering a lesson. Teaching experience and education were both found to be significantly associated with imagery use perceptions of PE teachers. These findings suggest that imagery is a skill PE teachers are employing, however not all PE teachers use it equally or for the same purposes.Acknowledgments: University of Winnipeg Major Research Grant
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".