College English Teaching Evaluation Model Using Natural Language Processing Technology and Neural Networks

Abstract

College English is one of the most important basic courses in college education, and classroom evaluation is one of the effective means to improve teaching efficiency. Multidimensional classroom evaluation system, which reflects the characteristics of college English courses, is the premise to standardize the evaluation process and ensure the fair and reasonable evaluation results. Based on NLP (natural language processing) technology and neural network, we use NLP to optimize BPNN (BP neural network) method to construct CETE (college English teaching evaluation) system model, which quantifies the concept of teacher evaluation index as input, makes the data clear, and takes the teaching effect as output. The training results show that the network can fit the training data well, and the prediction effect is remarkable, which indicates that the CETE model based on the BPNN method optimized by NLP is reasonable and feasible.