Research on Ideological and Political Teaching Evaluation in College English Courses Based on Data Mining

This study integrates data mining into the evaluation system for ideological and political education in college English courses. It uses Apriori, K-Means, and C4.5 algorithms to comprehensively analyze data on learning behavior, grades, and engagement from experimental and control groups. It is found that there is a 65% confidence link between studying online for over four hours weekly and improved scores in ideological and political tests; K-Means clustering divides students into high-scoring, medium-performing, and intervention-required groups; C4.5 reveals that visual learners with positive attitudes show a 23.6% increase in accepting ideological and political content. The experimental group does much better than the control group in English ability improvement, ideological and political literacy scores, and weekly study time. The research indicates that data mining techniques enable dynamic visualization of educational evaluation, support personalized teaching interventions, and provide quantitative basis for the precise implementation of ideological and political education.