Two-Phase Evaluation of the Validity of a Measure for Self-Regulated Learning in Sport Practice
Bibliographic record
Abstract
For full guidelines please refer to Given the potential role of self-regulated learning (SRL) for enhancing practice and expertise development, we aimed to advance a valid and reliable athlete self-report measure of SRL for sport practice. We built on Toering and colleagues’ (2012a) initial SRL instrument along with Bartulovic and colleagues’ (2017) sport-specific modifications, and created new items to extend the conceptual breadth of the subscales. With a multi-sport sample of 482 athletes (Mage = 26.45, SD = 12.66; 55% female), two analytic phases tested (1) the factorial validity of the initial and the extended inventories, and (2) criterion validity, by examining how SRL scores distinguished skill groups ranging from local to international competitive levels. In Phase 1, the initial measurement model demonstrated psychometric concerns and we opted to pursue a refined model. The extended model demonstrated acceptable factorial validity but resulted in the fewest subscales. In Phase 2, subscales scores from all three models generally distinguished international-level senior (18 + years) athletes from lesser-skilled groups. Integrating the psychometric evidence and between-group effects across the initial, refined, and extended models, we conclude that the refined inventory, the Self-Regulated Learning for Sport Practice (SRL-SP) survey, is the preferred instrument.
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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 | 0.044 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".