A Study on Current Conditions and Soccer Teaching Model for Elementary School Students in Demonstration Schools
Bibliographic record
Abstract
The purpose of this study was to study current conditions and soccer teaching model for elementary school students in the demonstration schools. This study used a qualitative method. Data collection was conducted by interview. Twelve informants involved with soccer instruction were selected by using purposive sampling technique from the demonstration schools. The informants consisted of four instructors, four student guardians, and four elementary school students. The interview result has been described its contents based on interview topics and it clarified descriptive information in three aspects as the followings: 1) As for the current conditions of soccer teaching model for elementary school students in the demonstration schools, the informants had different opinions either it was appropriate or inappropriate, 2) Soccer teaching model for elementary school students in the demonstration schools comprises of four prime aspects: instructor, lesson management, facility, and learners, 3) There are recommendations for further implementations of soccer teaching model for elementary school students in the demonstration schools to apply in the future. The findings from this study are useful for soccer instructors at the elementary school level or for instructors in educational institutes at various levels, including those who are interested in using as a guideline to improve their soccer teaching model in case it is appropriate to their students.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".