Effect of self-modeling and self-controlled feedback on the performance of professional swimmers and waterpolo players
Bibliographic record
Abstract
Self-modeling (SM) and self-control (SC) feedback can be presented as two solutions for learning improvement. Therefore, the aim of the present study was to investigate the effects of SM and SC feedback on 100-m freestyle performance of professional swimmers and waterpolo players. 25 elite male swimmers and waterpolo players, were randomly assigned to four groups: swimmer group with SM, swimmer group with SM and SC feedback, waterpolo players group with SM, and waterpolo players group with SM and SC feedback. 100-m freestyle times and performance were recorded. SM and SC feedback for the participants were utilized at the acquisition stage. The device used included a Lenovo B570 laptop and an Exilim ZR200 canon camcorder. SM and SC feedback presented to the swimmers and waterpolo players led to improved speed and results, and the effect of presenting SM with SC feedback to swimmers had better results. In conclusion, the present study indicates that SC modeling of watching video is a suitable method for professional swimmers. Water polo trainers can also use SM and SC feedback to enhance their players' swimming technique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".