An integrated teaching method of college physical education based on multi-classification support vector machine
Bibliographic record
Abstract
The purpose of this paper is to evaluate the teaching of university sports physical-educational integration based on multi-classification support vector machine (SVM).Firstly, it introduces the background of the development of university sports physical-educational integration teaching and the current situation of related research, and clarifies the purpose and significance of the study.Then the basic principle of SVM and the theory and application of multi-classification SVM are elaborated in detail, and the application prospect of multi-classification SVM in the field of educational evaluation is discussed.On this basis, the index system applicable to the evaluation of the teaching and learning of the integration of university sports and physical education is constructed, and the data collection and pre-processing methods are introduced.Subsequently, the process of constructing the teaching evaluation model for the integration of university physical education in sports based on multi-classification SVMs is described.Finally, descriptive statistics and analyses of the empirical data were carried out, and the empirical results of the multi-classification SVM were discussed and interpreted to explore the application of the evaluation model in university physical education integration teaching.Finally, the research results and conclusions are summarised, and the future directions and trends of the evaluation research on the integration of teaching and learning in university sports body-education are looked forward to.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".