Self-regulated learning and expertise development in sport: current status, challenges, and future opportunities
Bibliographic record
Abstract
In sport, athletes engage in large amounts of practice to reach higher levels of performance. Self-regulated learning (SRL) could be critical for optimizing training conditions and maximizing training amounts. Our purpose was to review literature concerning SRL in sport training contexts. We focused on articles taking a practice-enhancement orientation from a social-cognitive perspective. Thirty-four articles met search criteria. Most articles used a conceptual model guided by Zimmerman's work. We identified six emergent lines of inquiry: (a) descriptions of SRL; (b) SRL as characteristic of athletes; (c) skill group differences in SRL; (d) interventions with SRL as a focus or an outcome; (e) relations among SRL processes, beliefs, and other variables; and (f) measurement of SRL. Based on reviewed research in sport and drawing on research on SRL from education, we highlight four issues that provide opportunities for quality empirical research and conceptual development related to SRL and sport practice. In addition, we emphasize the potential role that SRL plays in sport expertise development.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".