Improving the Effectiveness of Foreign Language Teaching in Colleges and Universities Based on Multiple Data Chains

Abstract

Abstract To improve the effect of foreign language teaching in colleges and universities with high efficiency, this paper constructs a BP neural network optimized by multi-data chain by integrating multi-data chain and BP neural network, which effectively solves the shortcomings of BP neural network that is easy to fall into the local optimum. It optimizes the initial value and the threshold value of this neural network. On this basis, the multi-data chain optimized neural network is used to analyze the existing data related to English teaching in colleges and universities, to present the proportion of factors affecting foreign language teaching in colleges and universities in the form of data, and to summarize the reasons for acting the teaching effect and the problems existing in the current foreign language teaching classroom. Teachers and students make positive improvements to foreign language classroom teaching based on the data presented by the neural network to improve the effect of foreign language teaching in colleges and universities. Combined with the BP neural network optimized with multiple data chains, this paper conducted an experimental analysis of students’ concentration, satisfaction with the classroom, and performance. As can be seen from the results of data calculation, after the factors affecting the teaching effect were summarized and corrected, the concentration level of the two students in the experiment was significantly improved, and both remained above 50%. In addition, there was a massive difference in the grades of the same group of students before and after the improvement. The average grade of the control group was 76.47 and the middle grade of the experimental group was 87.6.