Determining the Factors of Teaching Effectiveness for Physical Education
Bibliographic record
Abstract
The development and maintenance of a physically active lifestyle and the promotion of health-related physical fitness have become two important components of the national standards for physical education. Physical education is one of the important aspects of the educational and teaching processes. This study was designed to determine the factors of teaching effectiveness for physical education by comparing the opinions of students on teaching courses of King Mongkut’s University of Technology, Thonburi with the totals of six aspects: (1) the purposes of Physical Education learning; (2) content and Physical Education programs; (3) teaching methods and teaching activities; (4) the personality aspects of Physical Education instructors; (5) equipment and facilities; and (6) assessment and evaluation. The sample included 1,000 students, with 558 male students and 442 female students, selected using the stratified random sampling method and divided into groups based on gender and types of sports. The results of the study showed that the highest percentage for the overall development of teaching physical education were the personalities of Physical Education instructors (59.18%) and the lowest percentage for measurement and evaluation had the lowest percentage (45.53%). The comparison between male and female students found that the course content and curriculum were at a minimal percentage for male students (45.42%), while the teaching methods and activities for female students (46.81%). Physical education specialists served as an important force in providing health-enhancing physical education for children and adolescents, as they are more effective than classroom teachers in promoting moderate to vigorous physical activity.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".