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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".