Innovative Research on the Development of Online Education Mode of Internet Thinking Based on the Discrimination of Learning Attention under the Analysis of Head Posture

Abstract

With the rapid development of Internet technology and the popularity of 5G and broadband, online education in China, especially mobile online education, is in full swing. Based on the development status of online education in China, this paper analyzes the innovative application of learning attention discrimination based on head posture analysis in the development of online education mode of Internet thinking. Learning attention is an important factor of students’ learning efficiency, which directly affects students’ learning effect. In order to effectively monitor students’ learning attention in online teaching, a method of distinguishing students’ learning attention based on head posture recognition is proposed. In the tracking process, as long as the head angle of the current frame is close to the head angle of the key frame in a certain scale model, the visual angle apparent model can reduce the error accumulation in large-scale tracking. A Dynamic Bayesian Network (DBN) model is used to reason students’ Learning Attention Goal (LAG), which combines the relationships among multiple LAGs, multiple students’ positions, multicamera face images, and so on. We measure the head posture through the similarity vector between the face image and multiple face categories without explicitly calculating the specific head posture value. The test results show that the proposed model can effectively detect students’ learning attention and has a good application prospect.