Factors Influencing Adjustment in Physical Education and Sports Learning after the COVID-19 Pandemic among Students in the Faculty of Education at Thailand National Sports University
Bibliographic record
Abstract
The purpose of this study is to examine factors influencing the adjustments made to physical education and sports learning among students in the Faculty of Education at Thailand National Sports University after the COVID-19 pandemic. 595 students were selected using stratified random sampling from undergraduates in the Faculty of Education at Thailand National Sports University during Academic Year 2020. The data were then analyzed in terms of descriptive statistics, Pearson correlation, and Stepwise Multiple Regression Analysis. The potential influences to the adjustments made to physical education and sports learning after the COVID-19 pandemic among students in Faculty of Education at Thailand National Sports University comprised these 5 variables: 1) activities to promote knowledge of COVID-19 prevention within the university; 2) the university’s policies promoting the prevention of COVID-19; 3) facilities within the university; 4) imitating a classmate’s adjusted behaviors; and 5) learning in class. These 5 factors could predict the adjustments in physical education and sports learning after the COVID-19 pandemic in the studied group with the percentage of 73.60. The significance predicted by equations were as follows: In terms of raw scores: Y/ = -0.175 + 0.384 (X3) + 0.265 (X1) + 0.224 (X4) + 0.084 (X5) + 0.064 (X6) In term of standard scores: Z / Y = 0.357 (ZX3) + 0.356 (ZX1) + 0.207 (ZX4) + 0.067 (ZX5) + 0.062 (ZX6)
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".