Convolutional Neural Network Based Network Distance English Teaching Effect Evaluation Method

Abstract

Aiming at the low accuracy of English teaching quality evaluation, a user perception evaluation modeling method based on convolutional neural network was proposed. Make full use of product usage data to establish quantitative mapping between teaching quality assessment and teaching quality to support product design improvement. On this basis, a convolutional neural network structure suitable for user perception evaluation model is established. Then, the optimal hyperparameters of the model were determined by k-fold cross validation analysis, and the overfitting problem of the model was improved. Finally, an example of user perception modeling for smart phones is taken to verify the effectiveness of the method. It is used for user perception assessment and prediction, which can help English teaching methods timely and accurately assess performance and provide decision support for English teaching improvement. SKYPE is currently the world's most popular Internet remote video, audio, text, and real-time interactive platform; the real-time audio and video communication is very clear; the remote education will expand to the family, especially to promote the remote universality and universal English education that can play a big role. In this paper, the basic function and performance of the platform are presented, and distance education in English presents a practical approach to the application aspects.