Data-driven Civic Education: Theory and Practice

The development of information technology has prompted big data to become an important force for educational progress. The purpose of this paper is to explore the application of data-driven technology in the field of civic education. Taking the civic education course of agricultural and water majors as the research object, K-prototype clustering algorithm, data mining technology and least squares support vector machine algorithm are applied to the construction of students’ portraits, recommendation of civic education resources and evaluation of the teaching effect of civic education, etc. respectively. Data-driven technology is utilized to promote the reform and enhancement of the Civic Education of Agricultural Water Specialties. In this paper, according to the data collected from the questionnaire survey, the algorithm is used to divide the students of agricultural water majors into 3 groups according to the level of ideological and political quality. According to the different characteristics of the three groups of students and the demand for civic and political learning, we recommend civic and political education resources to them, and the recommendation accuracy rate is about 90%. In the case, the teaching effect of Civic and political education in agriculture and water majors was evaluated as “good”.