A Teaching Effect Feedback Platform Combined with Convolutional Neural Networks

At present, the rapid development of information technology has promoted the diversification of education methods. Online education, as an important supplement to traditional education and an important means of education reform, has become an integral part of the education and teaching process. However, the long-term development of online education still faces many problems, such as difficulties in grasping students' classroom learning states, poor interaction between teachers and students, and difficulty in ensuring the teaching effect. Thanks to the development 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. In this paper, we propose a teaching platform combined with artificial intelligence. This platform uses CNN (Convolutional Neural Network) to recognize students' classroom behavior, specifically their facial expressions, to analyze their learning states and provide timely feedback on classroom teaching effects. The platform can also read video, use advanced image processing and pattern recognition to find expressions and gestures in the video, and mark them, thereby recognizing students' classroom statuses and monitoring their listening statuses. This platform can also generate student behavior reports and visual statistical charts to help teachers better understand students' learning situations and progress, provide teachers with accurate and real-time teaching feedback, help teachers adjust teaching strategies and methods, and improve teaching quality. Finally, after the preliminary test, it is confirmed that the platform has met the initial design requirements and has high identification accuracy and strong data analysis abilities.