Evaluation and Optimization of College English Teaching Effect Based on Improved Support Vector Machine Algorithm

Zhang Bao-Feng,Tongguang Ni

doi:10.1155/2022/3124135

Zhang Bao-Feng, Tongguang Ni

Open Access

https://doi.org/10.1155/2022/3124135

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Journal: Scientific Programming	Publication Date: Jan 28, 2022
Citations: 1	License type: CC BY 4.0

Affiliation: Shangqiu Institute of Technology

Abstract

Improved SVM algorithm improves the efficiency of College English teaching effect evaluation and meets the requirements of College English teaching evaluation. Based on the relevant theories, this paper constructs the evaluation index system with teachers and students as the main body and takes the questionnaire survey results as the input samples of the LSSVM algorithm. Compared with the evaluation accuracy of an optimized BP neural network and the category weighted gray target decision-making method, the results show that the evaluation accuracy of optimized LSSVM algorithm is 96.26%. Taking SIT as an example, this paper uses the optimized LSSVM algorithm to evaluate its teaching effect and obtains that teachers’ literature and teaching contents are important factors to improve the effect of English teaching. Therefore, this paper introduces the intelligent voice system to optimize the English teaching design of SIT. The teaching design is optimized from the dimensions of teaching objectives, learning situation, teaching content, teaching media and curriculum materials, and teaching procedures.

Highlights

Teaching effect evaluation has become a hot issue in the field of education at home and abroad
Experimental Results and Analysis is paper selects the English classroom teaching effect of Shangqiu Institute of Technology as the research object and collects sample data according to the evaluation index of CETE. rough the actual situation of College English teaching and the evaluation of College English teaching effect by experts, we can get the quality level of College English classroom teaching and 200 data samples for testing
E training data of each degree of the six faults are input to the LSSVM regressor for learning, while the Particle swarm optimization (PSO) algorithm is used for multiobjective optimization of the regularization parameters and kernel function parameters. e population size of PSO is set to 200, the learning factor to 1.49, the maximum number of iterations to 1000, and the inertia weight to 1 initially. e final choice of the best model parameters is (c, σ2) (2316.57, 224.46), b 9.93. e regression performance of the model is evaluated by the ratio of the number of correct model predictions to the total number of samples, as shown in the following equation: accuracy

Summary

Introduction

Teaching effect evaluation has become a hot issue in the field of education at home and abroad. Optimizing the evaluation model of college teaching quality based on the BP neural network will ignore the factors of teaching quality, resulting in the low accuracy of CETE evaluation.

Results

Conclusion