基于深度学习的学生课堂学习状态分析

Analysis of Students' Classroom Learning Status Based on Deep Learning

学生在课堂的学习状态是教学质量评价的重要依据。为精准地反馈学生课堂学习状态,该文利用YOLOX深度学习网络对课堂状况进行分析。首先,通过对上课学生进行人脸识别,统计学生出勤人数并对缺勤名单进行标注,实现学生从整体到个体的考勤评估;其次,通过识别学生的面部表情和肢体行为,检测所有学生的实时状态,完成学生从个体到整体的学习状态分析。上述研究表明,基于YOLOX深度学习网络的课堂状况分析数据能够准确地反映学生的学习状态,有助于任课教师及时改进和优化教学方式,从而提升教学质量和学习效率。

The learning status of students in class is an important basis of teaching quality evaluation.In order to provide accurate feedback on students'learning status in class,the paper used YOLOX deep learning network to analyze the class situation.Firstly,the number of students'attendance was counted and the absence list was labeled by face recognition of students in class to realize the attendance evaluation from the whole to the individual.Secondly,the real-time status of all students was detected by recognizing facial expressions and body behaviors to complete the analysis of students'learning status from the individual to the whole.The research showed that the analysis data of class situation based on YOLOX deep learning network can accurately reflect students'learning status and can help teachers improve and optimize teaching methods in time,and finally enhance teaching quality and learning efficiency.