Supervision system of english online teaching based on machine learning

Abstract

The automated supervision system for online teaching is volatile in current teaching observation. Hence, it requires additional comprehensive, analytical, and realistic discussion on how the automatic supervision method can be applied to high school teaching. This paper integrated remote supervision with machine learning algorithms (IRS-MLA) proposed for the online English teaching audit process. Here, IRS-MLA simulates the implementation of supervision methodologies in the teaching process according to English online teaching’s real needs. Furthermore, searching the performance and stating the learning process for students from the teachers’ perspectives and their students measures the teacher’s teaching process. This paper presents the studies for evaluating the classic English language online supervision and explores this method’s functional impact. This analysis’s findings show that the model developed in this paper worked well and validated based on the case study report. This study validates the proposed IRS-MLA with the highest performance ratio of 97.8%, the accuracy of 96%, the efficiency of 99.3%, and a success ratio of 98%, compared to existing models.