A teaching model of college ideological and political theory course based on multiuser detection algorithm

Abstract

Abstract To prove that the hybrid teaching mode of ideological and political theory courses in colleges and universities is significantly helpful in improving the teaching effect of Civics and Political Science, this study builds a basic framework of the hybrid teaching mode of the course by using the smart teaching platform starting from the accurate teaching mode of information technology. For the user tracking of the smart teaching platform, the gradient tracking multi-user detection algorithm based on the multi-step proposed Newton method is used to realize the user data analysis and the convergence analysis is conducted for the GDGP-MUD algorithm and MSQN-MUD algorithm. The results show that the maximum average response time is 460ms when the number of concurrent users of the smart teaching platform is 350-400. The average performance of the experimental class under the comparative analysis is 11.41 points higher than that of the control class.