The Effects of Self-Observation When Combined With a Skilled Model on the Learning of Gymnastics Skills
Bibliographic record
Abstract
In this experiment, we examined whether self-observation, via video replay, coupled with the viewing of a skilled model was better for motor skill learning than the use of self-observation alone. Twenty-one female gymnasts participated in a within design experiment in which two gymnastics skills were learned. One skill was practiced in conjunction with the self-observation/skilled model pairing and the other with only self-observation. The experiment unfolded over five sessions in which pre-test, baseline, acquisition, retention, and post-test scores were obtained. Analysis of the physical performance scores revealed a significant Condition ×Session interaction in which it was shown that there were no differences between the intervention conditions at baseline and early in acquisition; but, later in acquisition, those skills practiced with the self-observation/skilled model pairing were executed significantly better than those with only self-observation. Also, an error identification test showed that participants had significantly higher response sensitivity scores for those skills learned with the paired intervention compared to self-observation alone. These results suggest that pairing self-observation with a skilled model is better in a gymnastic setting than self-observation alone.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".