Research And Implementation Of Student Learning Status Feedback System Based On Facial Expression Recognition

The rapid advancement of information technology has spurred the diversification of educational methods.Online education has emerged as a crucial supplement to traditional education,and an indispensable means of educational reform.In the present day,online education has become an inseparable part of the educational and teaching process.However,the long-term development of online education still faces many problems,such as difficulty in grasping students’ classroom learning status,poor interaction between teachers and students,and inability to ensure teaching effectiveness.With the emergence of deep learning technology and the availability of large-scale facial expression databases,significant progress has been made in the field of facial expression recognition.It is widely used to analyze students’ learning status by recognizing students’ facial expressions.However,in the face of complex classroom recognition environments and possible frequent facial occlusion and angle deviation,current models find it difficult to ensure the accuracy and robustness of recognition results.The thesis focuses on the design and implementation of a learning status feedback system for middle school students in an online education environment.In response to the problem of complex environmental changes that cannot accurately recognize students’ expressions,a facial expression recognition model GVT based on Gabor convolution and Transformer is designed.A feature extraction block GVT block was designed by combining Gabor convolution with Transformer’s ideas.By using Gabor convolution to extract local facial features rich in texture and edge information,and then using Transformer to extract global dependencies between features,the model can better learn key facial features and significantly improve its classification performance.The accuracy of GVT on the RAF-DB and FER2013 Plus datasets is 88.56% and 87.38%,respectively.Comparative experiments and analysis with multiple other models have verified the superiority of this model.Finally,this thesisi designs and implements an online education student learning status feedback system based on the designed facial expression recognition model GVT.The system recognizes students’ emotional status in real time during class,maps the results to learning status,constructs a complete and objective evaluation of classroom status,and provides real-time visual feedback to teachers,so that teachers can adjust teaching strategies and content in a timely manner.The record of students’ learning status will also be saved in the database and visualized for easy reference and reflection by teachers and students after class.The student learning status feedback system designed in this article has powerful functions and good application value and reference significance.