Performance Analysis in Sports Training—Based on the Evidence in Computer-Assisted Instruction
Bibliographic record
Abstract
Nowadays, the application of various online platform has becoming a popular channel to reach the sports training purpose. Most of the scholars have studied the feasibility and advantages on using computer new media technology. But in reality, whether is it a perfect way to conduct sports training considering the particularity of sports program? And whether is it helpful and comfortable for people or students to learn and accept sports training? These two points are the key exploration in this study. Through model assumption and questionnaire survey conducted in the 4-week experiment and data process by Matlab, the author found out that there is an obvious inclination between sports programs. For programs that need cooperate with others or a team, the traditional face-to-face training instruction is still good enough and gets more preferences in students. While the programs that reply much on individual or personal skills do not have so much preferences as it is in the programs that need team players. Most of the scholars believe that the application of computer-assisted instruction is a good way to complete and substitute traditional education approach. But the results refute this thought to some extent. Compared with general program students, the preference differentiation is much more clear. Whether the computer-assisted instruction can really get the expected results is not totally determined by the ways how it is used, but by the program specialty itself in some degree.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".