Analysis of English Education Quality Evaluation and Internationalization Integration Based on Deep Learning

Abstract

English education is one of the most active research directions in the field of natural language processing. With the gradual implementation of deep learning in various fields, more and more industries have begun to use deep learning to carry out more efficient work. In the field of education, it is also urgent to adopt a more intelligent set of algorithms to relieve the pressure of teachers to correct test papers, and also to increase the fairness of non-subjective evaluations in the process of scoring. Teachers conduct teaching evaluation when the concept of teaching evaluation is not clear; there are defects in learning evaluation goals; there are many problems in the relationship between ability evaluation and knowledge evaluation; in the process of English teaching evaluation, the phenomenon of using summative evaluation instead of procedural evaluation is very serious. Therefore, this subject uses deep learning to study the problem of text line positioning and recognition. At the same time, this subject also builds a text scoring network based on RNN and STLM as a quantitative evaluation index for text line detection and recognition algorithms. We will examine students later. Whether problem-based learning theory can be used to promote deep learning among students to determine whether students’ systematic use of PBL in teaching can promote the use of deep learning in college English courses. Finally, comparing the effects of deep learning and shallow learning, it is concluded that the evaluation of deep English teaching can provide students with more learning opportunities, access to more learning-related materials, and questions are more transparent and are free, which is easy. It is speculated that the purpose of this problem is to facilitate the use of deep learning methods to find meaning types.