Cloud computing English teaching application platform based on machine learning algorithm

Abstract

Abstract Due to the progress of computer technology at this stage, English teaching has gradually achieved reforms with the help of the Internet. This paper builds an English teaching application platform based on machine learning algorithms, and realizes personalized online teaching with the help of cloud computing technology. In view of the complex characteristics of learners' online learning behavior data, this paper uses Pearson's correlation coefficient to quantify the correlation between various learning behavior variables. In order to further improve the accuracy of system data analysis, regression analysis is used to detect abnormal data points, and data with a data prediction deviation greater than 1 is determined as abnormal data. In order to reduce the loss of a large amount of data loss caused by the regression analysis to judge some anomalies, this paper uses the local outlier detection method again to detect local outliers, and compares the data repeatedly detected in the two identification algorithms, and judges it as abnormal data. special handling. In order to further verify the practicability of the system, network simulation is used to increase the number of concurrent operators in the system. The experiment proves that the click delay time of the English teaching video of the system is maintained below 1.8 seconds under 5000 concurrent operations, and the running speed of the system is significantly improved when the dynamic scheduling function is enabled, which proves that the system can meet the needs of multiple users for concurrent operations. Finally, a survey is conducted on the personalized education of the English teaching platform, and the relevant experimental and survey data are analyzed in detail, and a series of constructive strategies for the development of personalized English teaching in the cloud education environment are proposed.