Simulation of Dynamic Evaluation Model for College English Online Teaching Based on Machine Learning Algorithms

Abstract

College English online teaching is based on the integration of information technology, network technology, and disciplines. Based on the height of the times, it carefully examines the trend of social development, and proposes an action strategy to address the needs of future talent cultivation and the numerous problems faced by college English education, as well as to determine the direction and breakthrough of current education reform. It is an inevitable trend of modern education reform and development. This article conducts research on the simulation of a dynamic evaluation model for college English online teaching based on the ML (Machine Learning) algorithm. The research results indicate that among 20 students, 88% of the high group had longer online learning time than the low group. It can be fully explained that the improvement of students' grades is highly correlated with their online learning time, that is, students with longer online learning time have a greater improvement in their grades than students with less online learning time. Under the conditions of modern information technology, utilizing database networks and statistical techniques, combined with the theoretical achievements of testing and evaluation, corresponding dynamic evaluation systems are developed to conduct human-machine cooperation dynamic evaluation of teaching data at different stages. This can quickly and accurately reflect the development status of individual and group skills of learners, and provide targeted information for comprehensively promoting teaching reform.