Development of a Dynamic Allocation Model of English Teaching Resources Based on Combinatorial Mathematical Optimization Algorithm
Bibliographic record
Abstract
The study proposes a dynamic resource allocation model suitable for English language teaching, which combines learner characteristics, learning progress and resource availability to achieve real-time optimal allocation of resources through mathematical optimization algorithms.A multi-objective optimization model is constructed based on the key factors in resource allocation for English teaching.Facing the optimization objectives of maximizing learning efficiency and minimizing resource idleness, NSGA-II algorithm is used to construct a non-dominated solution to achieve global sorting, and combined with congestion calculation to complete global quality population screening.At the same time, the branch delimitation algorithm is utilized for local search of optimal solutions, and merged with the population of NSGA-II to generate the new generation of optimal populations.The optimization probability of the combined algorithm in this paper is 0.85, and the average convergence error is only 0.01081, which has excellent optimization performance.The resource allocation delay of this algorithm is around 0.1ms, and the allocation efficiency is more than 95%, and the comprehensive effectiveness is better than the comparison algorithm.The dynamic allocation model of resources in this paper improves the balance of resource allocation of English teaching and auxiliary room area, the number of teaching materials, the number of full-time teachers and teaching equipment.At the same time, it prompted the average English score of the experimental class to exceed 80, which was significantly higher than that of the control class.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".