Enhanced SVM Algorithm-Based Dynamic Early Warning System for College English Ideological and Political Course Education Using Machine Learning

Abstract

This study explores the use of an enhanced Support Vector Machine (SVM) algorithm to address challenges faced by students with lower academic performance in college English education, particularly in integrating ideological and political education. It develops a dynamic early warning system to provide timely support, employing targeted English courses and practical teaching methods. Methodologically, an improved SVM algorithm constructs a robust early warning model, enhancing monitoring and support in college English courses. The system's application contributes to advancements in machine learning and AI, emphasizing data-driven decision-making in education. Future research could explore scalability, long-term impacts, and further refinements to the SVM algorithm. In summary, the study successfully applies machine learning techniques to devise an innovative dynamic early warning system for English ideological and political education in college, offering valuable insights for practitioners and researchers alike in the realm of AI-assisted pedagogy.