A classroom student behavior detection model based on coordinate attention and fused residual networks along with its talent evaluation system
Yang Zhang, Changyu Du, Peng Ji, Lei Zhao, Qiao Sun, Chuanzhe Qu · 2023
With the continuous development of the Internet+ education concept and the rapid advancement of online, offline, and blended teaching technologies, the existing methods of evaluating students based on teachers' subjective opinions are insufficiently objective and lack a solid foundation. To improve this situation, we propose a classroom student behavior detection model based on coordinate attention and fused residual networks, along with its talent evaluation system. The classroom student behavior detection model, based on coordinate attention and fused residual networks, effectively combines the coordinate attention mechanism and residual networks. It highlights the relevant information in student image samples by effectively utilizing channel and spatial information. The talent evaluation system accumulates and analyzes classroom data through the first application terminal, forming a learning quality report that judges the quality of talent training and provides type information of talent training. Additionally, the system receives human resource information data through the second data terminal and performs employment classification based on talent information data, obtaining occupational tendency data. Therefore, this thesis will help enhance the reliability and accuracy of human resource judgment and increase the compatibility between human resources and careers.