Validity of self-regulated learning measure in predicting skill level differences
Bibliographic record
Abstract
Self-regulated learning (SRL) refers to athletes' active engagement in their own practice via planning, monitoring, and adapting processes (Zimmerman, 1986). SRL has been positioned as an individual difference variable impacting expertise development (Tedesqui & Young, 2015). McCardle et al. (2017) validated the structural validity of athletes' SRL self-report survey – the SRL-SRS for Sport Training (Bartulovic et al., 2017). Their measurement model, which also included earlier items from Toering et al.'s (2012) survey, showed acceptable model fit and divergent validity. This investigation aimed to examine the predictive validity of the same SRL-SRS for Sport Training model using skill level as a criterion outcome. Canadian athletes (n = 369; age 13 - 58 years) completed 53 SRL items and reported their highest performance level (local/regional, provincial, national, international). Multivariate analyses of variance tested for skill level differences on the constituent SRL processes (i.e., planning, checking, evaluating-reflecting, effort, self-efficacy) assessed in the survey resulting in significant differences: Wilk's ? = .915, F (15, 947) = 2.07, p = .009, partial ?2 = .029. Follow up discriminant analyses showed differences between the international athletes; self-efficacy and effort were the strongest contributors to the discriminant function, with evaluating-reflecting also contributing. A pattern emerged where local/regional athletes reported more engagement in many SRL processes than provincial athletes, and more SRL on certain processes than national athletes. Results are discussed in terms of remaining steps in SRL-SRS validation, measurement development, and limitations that may constrain effect sizes.Acknowledgments: This work was supported by a Social Sciences and Humanities Research Council of Canada Insight Development Grant 430-2015-00904 (Bradley W. Young, PI).
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".