Design and Implementation of Precision Strategy for Civic Education of Student Management in Colleges and Universities Based on Big Data Analysis

Educational management and civic education lead the cultural construction of the new era, implement the fundamental task of establishing moral education, and help improve the effectiveness of educational reform. In this paper, through the K-means algorithm to analyze the daily behavior of students in colleges and universities clustering analysis, the use of multiple regression model for the prediction of the performance of students’ Civic and political education in colleges and universities, respectively, to achieve the mining and analysis of big data in the student management and Civic and political education in the two aspects, and as a data support, put forward the precise strategy of the education management and the course of the Civic and political education of the same cultivation of human beings. According to the proportion of time spent in different types of locations, students are clustered into three categories: hard studying, lack of socialization and physical exercise, accounting for 36%, 38% and 26% respectively. Pearson correlation analysis was carried out between eight selected courses and Civic Education scores, and the correlation coefficients between each course score and Civic Education scores were between [0.51,0.69], with moderate correlation, and basically there was no multiple covariance between the course scores, which could be processed by regression analysis. The MSE and MRE of the resulting multiple regression model at the time of training are low, with maximum values of 48.164 and 0.075, respectively, indicating that the model has high prediction accuracy and can be used for Civic Education achievement prediction. The educational management and curriculum Civics collaborative parenting strategy proposed in this paper is of great significance for strengthening student management and improving the level of Civics education in colleges and universities.