Optimization of Teaching Quality Improvement Path Based on Recursive Algorithm in the Construction of Higher Vocational English Gold Classes under the Background of Industry-Teaching Integration
Bibliographic record
Abstract
The integration of industry and education refers to the in-depth integration of industry and education, which emphasizes the cultivation of students' practical ability and vocational quality.The integration of industry and education brings a new development direction for higher vocational education, and at the same time puts forward higher standards and requirements for higher vocational English teachers.In this paper, a new recursive Bayesian network structure algorithm is proposed based on RAI algorithm and CS algorithm, which mainly learns the Bayesian network structure by calling two functions recursively.Then based on the application effect evaluation model of recursive Bayesian network, the index system of classroom teaching evaluation is given based on the characteristics of classroom teaching, and the application effect is evaluated.The experimental results show that the optimization of the Bayesian network model can significantly improve the classification recognition reliability of the classifier, and taking the appearance score as a random effect, it can be found that the teacher's appearance difference has a significant effect on the teaching evaluation.The results of the study are of great significance to the construction of scientific English classroom construction as well as teaching quality evaluation system.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".