Cloud Computing Based Online Video Rhythmic Gymnastics Course Management

Abstract

Abstract In this paper, we utilize cloud computing to measure the best features in the Rhythmic Gymnastics dataset and compare them to obtain the most relevant feature information. By measuring the degree of uncertainty of random variables and calculating the course recommendation of target students relative to those in the subgroups, the clustering results of Rhythmic Gymnastics students were integrated with the target students’ performance information to derive the probability of Rhythmic Gymnastics courses appearing in the subgroups. Weight control, physical fitness, and good shape were significantly altered, and 95.66% of the Rhythmic Gymnastics students who took the course felt more flexible. Integration of online videos from cloud computing with the Rhythmic Gymnastics course makes full use of various media forms to provide more learning opportunities for students.