Effect of Self-regulated Learning on Class Performance and Test Score among Physical Education College Students in China: A Pilot Study
Bibliographic record
Abstract
Theoretical examinations are a critical gateway to public institution employment and qualifications in China, presenting a known challenge for physical education (PE) majors who typically demonstrate high practical aptitude. This study evaluates the efficacy of a self-regulated learning (SRL) intervention in improving the theoretical academic performance of college PE students. A sample of 24 students was randomly allocated to an experimental group (SRLG) or a control group (CG) receiving traditional instruction. The two-week intervention's impact was measured through pre- and post-assessments of class performance and test scores. Data analysis using ANOVA with Bonferroni correction showed statistically significant post-intervention gains for the SRLG compared to the CG. Simulation via generalized estimating equations (GEE) suggested sustained benefits. These results posit that structured training in self-regulated learning is an effective tool for bridging the theory-practice performance gap in PE education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 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".