| The informatization and intelligentization of higher education management in China are developing rapidly.The traditional way of collecting and analyzing classroom data such as students’ attendance,class status and class feedback by manual labor is time-consuming and laborious,and the collection channel is narrow,the quantity is small,and the analysis lags behind.As a result,school administrators cannot perceive the classroom situation in real time and accurately,and cannot timely adjust management measures.Therefore,the development and deployment of classroom data collection and visual management system is of great significance to strengthen classroom order management and improve teaching quality.This paper presents a basic framework of classroom data acquisition and visual management system,which consists of small program,analysis and processing algorithm and visual display module,and develops a prototype system.First of all,the classroom data collection end based on We Chat mini program is responsible for collecting the classroom data produced by teachers and students and storing them in the database and back-end file system.Secondly,using deep convolutional neural network based Mini＿Xception algorithm to recognize the students’ face expression in the classroom images captured by monitoring,and then to judge the students’ status in class.This paper also proposes a discriminant method for "attentive" expression based on Mini＿Xception algorithm for classroom teaching scenarios.Finally,the visualization module is implemented based on Springboot framework,and the classroom situation is displayed,counted and analyzed based on database and data processed by Mini＿Xception algorithm.The research shows that the system can effectively reduce the cost of classroom perception,so that the managers can obtain the classroom dynamics in real time,accurately and comprehensively,which has a great role in promoting the reform of classroom teaching and the improvement of teaching quality. |
PDF Full Text Request