The Construction of Online Course Learning Model of Ideological and Political Education for College Students from the Perspective of Machine Learning

Abstract

Student behaviour analysis in the classroom is an important part of teaching and educational innovations that can assist the institution find an effective strategy to improve students’ learning efficiency and ability to innovate. In this study, a human behavior recognition system is proposed for monitoring the learning status of students in the course of ideological and political education using the signals of smartphone embedded gravity sensors. A convolution neural network (CNN) is used to automatically extract prominent patterns from the raw signals of smartphone embedded sensors followed by the classification of the seven student activities including walking, going upstairs, downstairs, lying, sitting, standing, and running, respectively. The optimized CNN model was obtained after training on 1,500 training samples of student’s behavior data. The model is evaluated in terms of evaluation metrics such as accuracy, precision, and recall. The proposed model achieved 97.83% accuracy, 97.82% precision, and 97.83% recall, respectively, which are significantly higher than the classification performance of the other recognition models. The proposed model achieved inspiring performance compared to the existing behavior recognition systems. The model of human behavior can obtain the learning state behavior of the students from the college students’ listening equipment, to understand the learning situation of the students.