Self-regulated learning predicts final grades in varsity student-athletes, but is there more than meets the eye?
Bibliographic record
Abstract
Self-regulated learning (SRL) involves implementing goal-directed processes of self-awareness and control in pursuit of learning goals. SRL relates to achievement in several domains, but the relative value of domain-specificity in measurement remains unclear. This study examined the relationship between SRL and academic performance among incoming university student-athletes, while comparing measures varying in domain-specificity. Two cohorts (2017, 2020) of participants (N = 114; M-age = 19.2, SD = 1.0) completed two SRL surveys mid-year: (a) the Self-Regulation of Learning Self-Report Scale (SRL-SRS; Toering et al., 2012), a more dispositional measure from sport research; and (b) the Regulation of Learning Questionnaire (RLQ; McCardle & Hadwin, 2015), a more situational measure from education research. Linear regression was used to assess and compare prediction of end-of-term grades. When subscales from each survey were block entered separately, the SRL-SRS (R-squared = .193, p = .001) and the RLQ (R-squared = .192, p .100). Each survey still significantly predicted grades after controlling for cohort (SRL-SRS: p = .007; RLQ: p = .009). Findings suggest (a) dispositional and situational SRL measures similarly predicted student-athletes' academic success, (b) SRL sub-processes behave synergistically, and (c) SRL is influenced by a cohort's broader learning context.Acknowledgments: This project is supported in part by funding from the Social Sciences and Humanities Research Council of Canada.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".