The realization of information technology-based big data analysis in personalized ideological and political education in colleges and universities

This paper uses data mining technology to fully understand the hidden information in the data of students’ daily behaviors and students’ performance in the teaching system of colleges and universities, and excavate the potential value to realize the personalized education of ideology and politics in colleges and universities. The collected data are processed by preprocessing technology and then statistically analyzed, and then the correlation between students’ consumption habits and grades is studied by the improved K-means clustering algorithm and Apriori association rule algorithm. The analysis shows that the number of students whose ideological and political scores are in the high score range of 80-93 points or more is high. The data on one-card consumption can be clustered into four categories. The 10 enhancements of the correlation relationship between students’ grades and behavioral data are all greater than 1, and the degree of correlation is good. In the practice of personalized education in Civics, there is a significant difference between the performance of the experimental class and that of the control class (P=0.004).