Research on the Optimization Model of the Content Delivery of College Students' Civic Education in the Online and Offline Education Environment

In both online and offline educational environments, learning behavior data recorded by platforms can be analyzed to determine students' actual learning status, which is beneficial for optimizing educational models and improving teaching effectiveness. This paper investigates the delivery of ideological and political education content to college students, constructing an XGBoost prediction model optimized using the TPE method. Combined with the K-means algorithm and SHAP explanation method, it achieves prediction of the effectiveness of ideological and political education content delivery and analysis of influencing factors. The accuracy rate, misclassification rate, recall rate, precision, F1 score, and AUC value of the TPE-XGBoost predictive model are 91.74%, 8.96%, 93.75%, 91.22%, 0.9247, and 0.9813, respectively, enabling effective prediction of the effectiveness of ideological and political education content delivery for college students. Additionally, the K-means algorithm clustering identifies four categories of ideological and political education content delivery for college students. Additionally, the SHAP value for the feature “teacher professional competence” was the highest, followed by features such as “content timeliness and relevance” and “willingness to translate knowledge into action,” indicating that indicators such as teacher professional competence, content timeliness and relevance, and willingness to translate knowledge into action influence the effectiveness of ideological and political education content delivery. Based on this, an optimized path for the delivery of ideological and political education content for college students was designed, providing new insights for optimizing the teaching effectiveness of ideological and political education in higher education institutions.