Web-based optimization algorithm for web-based platform course design in teaching reform of university mathematics
Bibliographic record
Abstract
In this paper, we first constructed a mathematics network course goal achievement index system with 5 primary indicators, 16 secondary indicators and 70 evaluation points to provide a scientific basis for course design.After that, based on the radial basis function (RBF) neural network structure, the fruit fly optimization algorithm (FOA) is introduced to dynamically optimize the parameters of the RBF model, and the dynamic FOA-optimized RBF neural network model is put forward to predict the degree of achievement of the course objectives.The results show that the model in this paper has good convergence and prediction accuracy, and its error on the four course math network goal attainment is only about 0.4%, with excellent model accuracy and simulation effect.Combined with the prediction results, considering the shortcomings of the current teaching, a blended teaching model based on mathematics majors is constructed, and the advantages of the teaching scheme in this paper are elaborated, which provides support for the teaching reform of mathematics courses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".