Analysis of the practical education model and its value in the context of big data in university civic education

Abstract

In order to verify the feasibility of data mining for the practical education model and its value analysis of college civic education in the context of big data. Based on the big data mining algorithm-parallel density clustering algorithm, this paper designs five types of teaching evaluation indexes of Civic Education as experiments to explore the new direction of Civic Education practice in colleges and universities in terms of value perception and target, teaching content, and method, teaching interaction, and organization, learning input and support, and learning effectiveness and evaluation. The experimental indexes show that: through the big data mining algorithm, we can understand that the current students have relatively good feelings towards “Civic Education” and “Introduction” courses, with mean values of 4.03 and 4.01, respectively; they prefer current issues in teaching contents and methods, with mean values of 7.48; they prefer current issues in teaching contents and methods. The mean value is 7.48; the mean value is 5.45; the mean value is 5.12; the mean value is 5.12; the mean value is 6.85; the mean value is 6.85; the mean value is 6.85. The results also show that the big data parallel density clustering algorithm can provide the relevant data mining analysis for the college Civic Education practice. The results also show that the big data parallel density clustering algorithm can provide relevant mining data analysis for college thinking and political education practice and can present the innovative reform direction of college thinking and political education through data more intuitively. It also makes the college thinking education truly enjoy the dividends of the big data era.

Keywords

• big data mining algorithm
• parallel density clustering algorithm
• university civic education practice
• education evaluation index
• data analysis
• 65y04