Effectiveness Assessment of College Ideological and Political Courses Using BP Neural Networks in Network Environment.

Abstract

We will not be able to provide educators with the assistance they require to implement the network IPECU and boost the effectiveness of the network IPE until we have established a trustworthy and effective assessment system. By first identifying the problems with the current ideological course assessment system, this study builds an indicator system for evaluating IPECU's effectiveness. An evaluation model of IPECU effectiveness based on the BPNN is designed and built using this as the basis. In this research, GA with adaptive mutation is used to optimise the initial weights and thresholds of the BPNN. As a result, the training termination conditions are satisfied by the weights and thresholds for the BPNN's assessment of the instructional quality more quickly, increasing the prediction accuracy and convergence speed. The simulation and comparative analyses presented in this study use MATLAB to verify the effectiveness of the method's evaluation. Experiments show that this algorithm's prediction accuracy can be as high as 95%, which is comparable to GA and the traditional BPNN algorithm. This method could provide some technical assistance for the network IPECU validity assessment.