Big Data-Driven Precision in College Ideological and Political Education: Design and Practice of Personalized Guidance Programs in Student Management Scenarios
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Abstract
Big data-driven precision ideological and political education uses multidimensional student data to meet the demand for innovative talent cultivation in colleges and universities. Based on the theoretical logic of big data technology and personalized ideological and political education, this paper focuses on student-management scenarios and constructs a personalized guidance program featuring “data collection-intelligent analysis-precise delivery-feedback optimization.” By integrating behavioral data, ideological dynamics, academic development data, and psychological information, machine learning algorithms are used to build students’ ideological and behavioral profiles, enabling precise matching of educational content, methods, and timing. Taking 2,000 students from three different types of colleges as research subjects, an empirical study conducted over one academic year shows that the program significantly improves students’ ideological recognition, behavioral standardization, and academic achievement rates. The results verify the feasibility and effectiveness of big data-driven precision ideological and political education and provide a replicable paradigm for scientific transformation of student management in the new era.
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