A study of classroom learning attention discrimination method based on head posture detection
Ge Chen, Jianqiang Ji, Tianyi Wang · 2023
To monitor and analyze students' classroom attention, an approach of discriminating attention of the students based on the recognition of head position is presented.. The facial feature points of classroom monitoring images are detected using convolutional neural network, the face is tracked using ( pose from orthography and scaling with iterations, POSIT) algorithm, the rotation angle of the face is calculated, and the student's attention is analyzed based on the tilt angle of his or her head. The proposed model has an accuracy of 88.7% for attention detection, which allows for effective analysis of students' attention and provide a basis for evaluating course teaching.