An Empirical Study of Intelligent Algorithms for Evaluating English Teaching Effectiveness in Colleges and Universities
Bibliographic record
Abstract
Objective and comprehensive teaching effectiveness assessment is a strong guarantee for the quality of English teaching in colleges and universities.This paper establishes an index system for evaluating the teaching effectiveness of English in colleges and universities from the levels of students, teachers and classroom teaching.The particle swarm algorithm is used to optimize the convolutional neural network, the SGD formula is used to improve the calculation accuracy, the Adam optimizer is improved to improve the model operation efficiency, and the optimization algorithm of convolutional neural network, PSO-CNN, is proposed.The PSO-CNN algorithm is introduced into the system and the logic design is carried out to realize the evaluation of the teaching effect of English teaching and to build up the English teaching effect evaluation system based on the intelligent algorithm.Evaluation system based on intelligent algorithm.The performance of the system is examined and analyzed with the help of PCA method, which shows that the cumulative contribution rate of the first six indicators, such as learning acquisition and teaching ability, reaches 91.08%.In the mean square error of model training, the PSO-CNN algorithm applied in this paper's system has a lower mean square error than other algorithms after 35 iterations, and has better evaluation accuracy.In the application practice of English majors in a higher education institution in B city, after applying the system of this paper to evaluate the effectiveness of English teaching, the English test scores of the regular and experimental classes with improved teaching were significantly improved.
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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 | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".