Evaluation Model of Educational Curriculum in Higher Schools Based on Deep Neural Networks

Abstract

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.