A high-performance computing-supported model for monitoring the effect of ideological education in a big data environment

This paper constructs a set of models for monitoring and evaluating the effect of Civics education through the research on the evaluation of Civics education based on educational big data environment.First, based on distributed gray cluster analysis, it analyzes and researches students' Civics learning behavior, and explores learners' learning characteristics by mining meaningful behavioral features for cluster analysis.The second is to design the Civics teaching quality evaluation model using principal component analysis, test the effects of population size and convolution kernel number on the performance of the Civics teaching quality evaluation model, and optimize the teaching quality evaluation model by using the dimensionality-reduced evaluation data.Distributed gray cluster analysis gets four clusters according to the characteristics of students' learning behaviors, which are divided into excellent, diligent, average, and negative students.PCA selection of evaluation indexes found that the cumulative contribution rate of the first 10 principal component indexes to the evaluation of the quality of Civic Teaching in colleges and universities has reached 95.63%, which indicates that these 10 indexes can adequately evaluate the quality of Civic Teaching in colleges and universities.When the number of population size is taken as 31 and the number of optimal convolution kernels is taken as 19 values, the RMSE of the evaluation model is 0.01973, and the test time consumed is 0.0783ms, which is the best performance.The constructed Civics education effect monitoring model can effectively assess students' learning behavior and efficiently and accurately evaluate the quality of Civics teaching.