Study on the Grading Performance Evaluation Model of GWO-SVM Algorithm
Teacher classification and grading performance evaluation is the key to improve teach-ing quality and promote educational equity.However,the current performance evaluation of teacher classification is limited to classroom teaching,ignoring the overall role of teachers in teaching activities.Therefore,the comprehensive teaching activities are included in the evaluation index system,and we im-prove the parameter selection of SVM based on the grey Wolf optimization algorithm to improve the ac-curacy and convergence speed of the evaluation model.In the experimental results,the convergence rate of the improved SVM model is improved by 75.38%,and the average error of the evaluation results is reduced by 8.08%.The experimental results show that the proposed evaluation model not only has higher accuracy,but also has better convergence speed performance,which provides a new technical means for the objective evaluation of teacher performance appraisal.
grey Wolf optimization algorithmsupport vector machineteacherperformance ap-praisalevaluation model