Using Bayesian network modeling to analyze the link between students’ psychological changes and athletic performance in physical education
Bibliographic record
Abstract
The relationship between students' psychological changes and athletic performance in physical education has an important impact on teaching quality.Traditional research methods are difficult to accurately portray this complex nonlinear relationship.In this study, a Bayesian network model was constructed based on the improved MMHC algorithm to analyze the association between students' psychological changes and sports performance in physical education.A stratified whole group sampling method was used to collect data from 2,480 students from 32 high schools in 16 cities in Shandong Province, using the Canadian Assessment of Physical Literacy Questionnaire (CAPL-2) and the Symptom Self-Rating Scale (ACL-90).The traditional Bayesian network was optimized by the event extraction algorithm with the improved MMHC algorithm to establish a network topology containing 17 measures.The results showed that the model prediction accuracy reached 90.37%, and the number of days of participation in moderate-and high-intensity activities in a week had the greatest impact on the mental health level, with a decrease of 9%.Sensitivity analysis showed that four factors, including the definition of health, safe behavior in performing physical activity, the correct way to improve motor function, and the time required to perform physical activity daily, were the sensitivity factors.The study reveals the causal chain of motivation and confidence → knowledge and understanding → daily behavior → mental health level, which provides theoretical support for the reform of physical 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".