Using Sport Education to Teach Wushu, a Form of Chinese Martial Arts
Bibliographic record
Abstract
This study examined the veracity of the commonly held notion that ‘there is only one way to teach Chinese martial arts.’ To achieve this, a cohort of Chinese physical education majors and their teacher participated in a semester-long season of Wushu taught using Sport Education (SE). Data were collected from the teacher in the form of weekly logs and interviews and students participated in small-group interviews throughout the program. Student grades were also analyzed. Student and teacher generated data were analyzed using analytic induction and constant comparison techniques. There was a high consistency among teacher’s log entries, her interviews, and comments made by students during interviews. Nevertheless, one topic that occupied significant discussion in the final interview was the teacher’s sense of professional renewal as a result of the SE project. Analysis of student interviews generated six themes, most which reflected student responses about SE (e.g., teams, competition, roles) but which also expressed a preference for the instructional climate of classes. Further, participation during the season did not compromise knowledge or skill performance of these students. Rather, SE led to higher levels of enthusiasm and engagement than in students’ previous martial arts courses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".