Classroom management of English distance education based on improved machine learning for classification and feature extraction in artificial intelligence

Abstract

            As globe revolves around Internet for infinite access to materials available in a remote region, today's era is referred to as Internet era. Internet has penetrated education sector to a greater extent; traditional offline classrooms are being replaced with online as well as offline classes to facilitate teaching as well as learning. Multimedia courseware is becoming increasingly significant in classroom instruction as part of the education integration movement. We look at how AI multimedia courseware can be used in the classroom and investigate the classroom teaching mode, with a particular focus on the features of AI multimedia courseware classroom teaching method. This research propose novel technique in classroom management for English distance education based on machine learning data classification and feature extraction. Here the classroom infrastructure data has been analysed initially. Then based on the opinion of students and their grades in English subject, the positioning has been carried out. Then the historical data of classroom monitoring has been collected and processed. The features has been extracted using back propagation neural network then based on the extracted features the classification has been carried out using genetic algorithm with regression model. Based on this classified data the student positioning has been carried out. This study uses comparative experiments to validate the performance of model to verify methods performance. Findings demonstrate that approach developed in this work has some effect on real-time datasets.