A study on modeling the association between students’ psychological changes and athletic performance in physical education based on numerical computational methods
Bibliographic record
Abstract
Mining the dynamic association between psychological state changes and sports performance is one of the core tasks of physical education towards scientific teaching.In this paper, the data of psychological change indexes of student athletes were collected by scales and the indexes variability was tested.Combined with the principal component analysis to extract the principal component factors of the psychological change index data, construct the correlation coefficient matrix, and calculate the multiple linear regression equations of psychological change and sports performance.The gray correlation model based on the whitening weight function was used to analyze the gray correlation between psychological change and athletic performance, and calculate the influence of the two.Among the 9 psychological indicators, 4 dimensions, such as social evaluation anxiety, had a significant difference with P<0.01.P<0.05 for 2 dimensions such as competition preparation anxiety, there was a difference.In the principal component analysis, the negative and positive psychological dimensions were extracted as principal components, including the 7 psychological indicator components excluding the 2 dimensions.Judging from the regression coefficients and gray correlation calculation results, the 3 psychological indicators of cognitive state anxiety, state self-efficacy, and injury anxiety had the greatest influence on sports performance.Targeted alleviation of cognitive and injury anxiety and improvement of self-confidence can optimize students' sports performance.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".