Evaluating the Effectiveness and Challenges of Ideological and Political Integration in University Aesthetic Education: An Online Survey Supported by Big Data

Abstract

In the context of educational digital transformation and the Ministry of Education’s requirement to build a full-staff, whole-process, all-aspect ideological and political education framework, this study addressed common issues in university aesthetic education—such as lagging teaching effect evaluation and rough learning condition analysis—by exploring the integration of big data technology with ideological and political education in aesthetic courses. Using an empirical approach, it surveyed and analyzed learning behaviors of 2,376 art students from 12 universities nationwide and adopted a three-phase closed-loop model (pre-teaching diagnosis, in-teaching integration, post-teaching optimization). The results indicated that big data lifted the classroom interaction rate by 54%, excellent homework rate by 66.4%, and that 41.7% of students achieved emotional resonance in red-themed art learning; disciplinary differences in value cognition also emerged. The study confirms that big data significantly boosts the effectiveness of ideological and political integration in aesthetic courses, offering practical references for optimizing teaching strategies.