基于人脸表情的在线学生课堂评价系统

ffected by the new coronavirus-infected pneumonia epidemic, a number of schools in China have been postponed the term, and normal teaching plans have been affected to varying degrees. In this context, the online education has been highly respected by various schools and educational institutions. Different from the traditional classroom education model, a major feature of online classrooms-the separation of teachers and students in time and space, makes it impossible for teachers to get learner feedback on the classroom and other issues at any time. This article designs and implements an online student automatic evaluation system based on facial expressions. In the design process, deep learning is used to build an expression recognition system that can recognize different emotions. The keras framework is used to complete the model. Then, based on the student\'s expression information and voice data generated in the real classroom, an estimated evaluation score for the course is made, and based on W & D recommendations The algorithm improves the classroom evaluation model, outputs the classroom quality score in the form of a percentage system, and finally applies the system to the online student classroom system.