An Intelligent Assessment Method of English Teaching Ability Based on Improved Machine Learning Algorithm

Abstract

In order to fully exploit the effect of intelligent evaluation of English teaching ability and explore the quality of English teaching, an intelligent evaluation method of English teaching ability based on improved machine learning algorithm is proposed, which can ensure the rational allocation of English teaching resources, analyze the big data of constraint parameters of English teaching ability evaluation, and obtain frequent item sets of English teaching quality based on big data mining technology. The particle swarm optimization (PSO) method was used to improve the parameters of the SVM, and the optimal parameters of the support vector machines (SVM) were obtained, which were input into the sample of English teaching effect evaluation, and a method was constructed to maximize the SVM. The optimal parameters are introduced into the decision function, so as to achieve the purpose of English teaching quality assessment. The experimental results show that the proposed method has a high convergence speed and can achieve rapid convergence with only 30 network trainings. At the same time, the evaluation accuracy of English teaching quality is as high as 95%, the correlation coefficient of the results is as high as 0.95, and the evaluation time is low. It is less than 150 ms, and the keyword frequency identification is better and better, which can realize the objective evaluation of online teaching quality.

Assessment and Prediction of University English Teaching Effect Using Progressive Recurrent Generative Adversarial Network

“…Narengerile and Di [16], have presented An Intelligent Assessment Method of English Teaching Ability Based on Improved Machine Learning Algorithm. Here, intellectual evaluation technique depend on an enhanced ML process was proposed in order to assess English teaching ability intelligently, investigate quality of instruction.…”