Self-regulated learning and expertise development in sport: current status, challenges, and future opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it