Smart Classroom Learning Analytics System Based on YOLOv5

With the continuous promotion of education informatization, the construction of intelligent teaching environment has become an important development direction in the field of education. This paper focuses on the intelligent classroom system based on YOLOv5, which aims to solve the traditional classroom observation problem by using artificial intelligence visual analysis technology. The system automatically identifies and counts six typical classroom behaviors of students through real-time parsing of classroom video streams, providing teachers with objective classroom participation analysis data and helping to improve teaching quality. The article describes in detail the principle of YOLOv5 algorithm, model training process, system architecture and functional implementation, analyzes in depth the optimization and innovation points of the system in terms of real-time, robustness and scalability, and verifies the effectiveness of the system through experimental data.