Compared Efficiency of the Class Management Appropriation Skill by Two Categories of Physical Education Trainee Teachers: The Example of Learning Time
Bibliographic record
Abstract
The purpose of this study is to compare the quality of skill’s appropriation of the learning time management by two categories of physical education (PE) trainee teachers coming from two different types of academic training. Ten volunteering teachers of PE with an average age of 23 ± 1 years and recently recruited (9 months work experience) were selected. Five licensees (ESL) originate from the License Master Doctorate (LMD) and five masters graduates (ESM). It should be noted that chosen teachers have significantly the same working conditions (two sessions of sport and physical education (SPE) per week, 32 to 34 students (±14 years) per class) and have the same physical or environmental conditions. The observation focused on three sports disciplines (sprinting, gymnastics and handball). Gender and experience in sporting practice of students have not been considered. The results obtained have shown that the ESM develop a control quality much better than their ESL counterparts balanced distribution of learning time, especially regarding the time spent on preparatory situations (p p < 0.047). Also, it should be noted that the licensee teachers spend more time explaining the objectives and content of the session than their counterparts ESM. However, the difference appears insignificant as to time provided for the organization of the material, the transition to the explanation of the mode of organization, movements, available equipment and to discuss the rules of discipline and security.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".