Classroom teaching quality evaluation system can enable the school’s functional departments to accurately assess the performance of the teaching staff and current teaching operations. As per the requirements for cultivating high-quality talents, planned teaching staff construction and teaching reforms need to be carried out to promote teachers’ appointments. Improving the system makes the appointment process more scientific by giving due attention to the individual characteristics of all types of teachers while hiring them for related jobs. The system motivates the love of teaching, high academic level, high teaching level, and competitive teaching. In recent years, the rapid development of artificial intelligence and deep learning caused many colleges and universities to put forward the target of campus digitization and education informatization. The state of the classroom is a critical reference factor throughout the teaching and learning process for evaluating students’ acceptance of the course and the quality of the teaching. However, at present, the analysis of the classroom status is mainly conducted manually, which distracts teachers and is also not much precise. Therefore, finding a method that can improve the efficiency of classroom status analysis has great research significance. This study uses the deep neural network method to read each class’s video recording and analyze it from the aspects of students’ behavior and attendance. The system can realize class behavior and eventually evaluate the course quality employed to motivate teachers to improve teaching and overall quality of education.

中文翻译：

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基于深度神经网络的高校教育课程评价模型

课堂教学质量评价体系可以使学校职能部门准确评估师资队伍的绩效和当前的教学运行情况。根据培养高素质人才的要求，有计划地开展师资队伍建设和教学改革，促进教师聘任。完善制度，在聘任相关岗位时，充分考虑各类教师的个性特点，使聘任过程更加科学。制度激发了热爱教学、学术水平高、教学水平高、教学竞争激烈。近年来，人工智能和深度学习的飞速发展，使得许多高校提出了校园数字化、教育信息化的目标。课堂状态是整个教学过程中评估学生对课程的接受程度和教学质量的关键参考因素。但是，目前课堂状态的分析主要是人工进行，分散了教师的注意力，也不够精确。因此，寻找一种能够提高课堂现状分析效率的方法具有重要的研究意义。本研究采用深度神经网络的方法，读取每个班级的录像，并从学生的行为和出勤等方面进行分析。该系统可以实现课堂行为并最终评估课程质量，从而激励教师提高教学和整体教育质量。