Research on the Application and Effectiveness of Big Data Analysis in Civic and Political Education

Abstract

This study explores the application and effectiveness of big data analytics in enhancing ideological and political education (IPE) using a mixed-methods approach. Addressing the pressing need for innovative educational strategies, our research is structured into three phases: data collection, analysis, and outcome evaluation. We gathered comprehensive data from student performance records, surveys, and social media interactions, employing stratified random sampling to ensure a representative sample. The data preprocessing stage involved rigorous cleaning and normalization, followed by descriptive statistics, predictive modeling using multiple linear regression, and cluster analysis via the K-means algorithm. Evaluating the outcomes, we measured improvements in student performance, shifts in attitudes, and changes in engagement levels, with statistical significance assessed through paired-sample t-tests. Our results revealed a significant enhancement in GPA (from 3.15 to 3.35), survey scores (from 65.2 to 72.5), and social media engagement post-intervention. This study underscores the potential of big data analytics to provide actionable insights into student behaviors and outcomes, thereby informing more effective IPE strategies.