Research on optimization strategy of dynamic planning method in resource balancing in online and offline integrated teaching in higher education
Bibliographic record
Abstract
Starting from the essence of dynamic programming algorithms, the terminology in dynamic programming algorithms, the applicability conditions of the algorithms, and common sub-problem models are summarized.The Belman optimal algorithm is used to split the multilevel problems in dynamic planning into simple single-level problems and solve them one by one, combined with the function approximation structure to approximate the performance index function, to construct the adaptive dynamic planning algorithm, and to apply it in the resource balancing optimization of integrated teaching.The results show that the adaptive dynamic programming algorithm has better resource balancing effect than other algorithms, and the number of convergence and running time are reduced by 6-53 times and 48.92-90.34seconds respectively.The introduction of the adaptive dynamic programming algorithm improved the resource balancing accuracy of university teaching and learning management by 4.0%-17.4% in each subject group.As the number of resources increased, the time consumption required when balancing resources decreased by 50%-83.33%for test groups 3, 4 and 5, and the efficiency of the test improved by 75%-100%.This shows that the algorithm proposed in this paper is effective when dealing with balancing online and offline teaching resources in higher education.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".