Construction of College English Teaching Environment Assessment Model Based on BP Neural Network and Multiple Intelligence Theory.

Abstract

College English has almost always been a required course, and a college student's level of English proficiency is one of the factors used to assess their learning capacity. The quality of students' English learning is largely influenced by the level of English instruction provided in colleges. However, there are still a lot of issues with college English instruction today, the most glaring of which is that English instruction is being overly simplified, and that the methods, modes, and purposes of instruction are also very narrow. Due to this, it is challenging for most colleges and universities' English teaching levels to satisfy the requirements of high-level education. The PSO-BP neural network model, which optimizes the BP neural network (BPNN), is used in this study to build a high-precision and diversified English teaching evaluation model in order to address the aforementioned issues. According to the experimental findings, the PSO-BPNN algorithm has a relative error of just 0.29 percent and an average accuracy rate of 97.02 percent. Overall performance is superior to that of the conventional BPNN algorithm, and it is the most adaptable in terms of creating various evaluation modes.