The Effects of the Teaching Games for Understanding (TGFU) Mode Adopted in A College Basketball Program
Bibliographic record
Abstract
Basketball, as one of the most popular sports courses in Chinese colleges and universities, has always been taught in a traditional ball teaching method, which has caused many problems in students’ learning effects, such as poor basketball tactics, weaker physical fitness and so on. Therefore, it is imperative to reform the basketball courses in colleges and universities. Accordingly, this paper is aimed to explore the effects of the Teaching Games for Understanding (TGFU) method in basketball courses on male students from the Class of 2023, a university in Shanghai, China, in terms of their basketball tactics and physical fitness. The study sampled 60 students randomly selected from 1850 students who took the optional basketball courses. The selected students were divided into two classes, either with 30 students: the experimental classes taught with the TGFU method and the control class taught with the traditional method. The data were collected from the pre-tests and post-tests of both classes and analyzed by the tool Statistic Package for Social Science (SPSS 24.0). Including the calculation of average values and standard deviation for T-test. It is found that the TGFU adopted in the basketball courses has significantly improved students’ basketball tactics and physical fitness, and that this method is advised to apply in college courses for students of all grades.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".