An analysis of the relationship between self-efficacy and performance in a continuous educational gymnastics routine
Bibliographic record
Abstract
Research has consistently shown a moderate, positive correlation between efficacy and sport performance (Moritz, Feltz, Fahrbach, & Mack, 2000). This relationship has been shown to be reciprocal over seasons (e.g., Myers, Payment, & Feltz, 2004), and across trials (e.g., Feltz, 1982). The purpose of the present study was to examine the self-efficacy-performance relationship within one continuous routine. Forty-seven undergraduate students (27 male, 20 female) performed a gymnastic routine while using a self-efficacy scale following a 9-week educational gymnastics course. A path analysis revealed that self-efficacy was not a significant predictor of performance, nor was performance a significant predictor of self-efficacy. However, previous performance was a significant predictor of subsequent performance (p < .01; ?s ranged from .517 to .679). These findings are consistent with previous research suggesting that past performance is a stronger predictor of future performance than self-efficacy (Feltz, 1982; Feltz & Mugno, 1983). Self-efficacy may not be a significant predictor of performance due to performance barriers which prevents efficacy from operating as a causal influence (Feltz, 1982), or self-efficacy may be embedded in previous performance which could inflate past performance scores (Feltz, Chow, & Hepler, 2008). Future studies will address limitations in protocol when studying self-efficacy and performance within continuous trials.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".