A Methodological Study on the Quality Assessment of Online-Offline Blended Teaching in Physical Education Courses Using the Assignment Approach
Bibliographic record
Abstract
In order to overcome the shortcomings of traditional physical education teaching quality assessment methods, this paper proposes a hybrid online-offline physical education teaching quality assessment method based on the assignment method.The method utilizes the hierarchical analysis method (AHP) to initially assess the quality of hybrid physical education teaching, and introduces the improvement of the pull apart step method (ISD) to improve the assessment accuracy of the hierarchical analysis method.The AHP and ISD methods are weighted to form a comprehensive integrated assignment method to construct a hybrid physical education teaching quality assessment model.Finally, the accuracy of the teaching quality assessment model was tested by the plain Bayesian classifier (NBC).The questionnaire data from teachers and students of an engineering university were collected and applied to the model of this paper, and the final results show that the model of this paper can effectively realize the grade assessment of hybrid physical education teaching quality according to the obtained data.The simple Bayesian classifier used in this paper has obvious performance advantages compared with multiple linear regression (MLR) models.The application of the method in this paper can effectively meet the needs of teachers and students in mixed physical education teaching and learning, and at the same time, it can significantly improve students' physical education performance, which is highly welcomed by teachers and students in schools.
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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 | 0.042 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".