Design of English Learning Effectiveness Evaluation System Based on K-Means Clustering Algorithm

Abstract

English is the universal language of the world. In the context of global economic integration, English learning is not only an essential course for business elites but also a required course for the general public. Currently, in colleges and universities across the world, English is presented as a compulsory first foreign language course. Therefore, how to improve the effect of English performance assessment in the context of smart teaching has become an important part of smart English teaching. Due to the influence of interference factors, human factors, or external factors, the traditional English language teaching evaluation system has the problems of high system sensitivity, long envelope delay jitter time, and short stationary state maintenance time. Therefore, this study develops an English learning effectiveness evaluation system based on a K-means clustering algorithm. The SQL Server 2005 database management software is used to develop the system database; various functional modules of the system are designed using ActiveX, with emphasis on the design of scoring functional modules; and different roles and permissions are given to administrators, teachers, and students. A student English learning effectiveness evaluation model based on BP neural network training and K-means clustering algorithm is designed to optimize the English learning effectiveness evaluation model and achieve effective English learning by solving the consistent estimate of the effectiveness of English learning assessment. The performance test results show that the proposed system has a lower sensitivity coefficient, a shorter envelope delay jitter time, and a longer period of steady-state maintenance, indicating that the system can achieve stable operation.