Personalized Learning Ability Classification Using SVM for Enhanced Education in System Modeling and Simulation Courses

This study investigates the application of a support vector machine (SVM)-based model for classifying students’ learning abilities in system modeling and simulation courses, aiming at enhancing personalized education. A small dataset, collected from a pre-course questionnaire, is augmented with integer data to improve model performance. The SVM model achieves an accuracy rate of 95.3%. This approach not only benefits courses at Guizhou Minzu University but also has potential for broader application in similar programs in other institutions. The research provides a foundation for creating personalized learning paths using AI technologies, such as AI-generated content, large language models, and knowledge graphs, offering insights for innovative educational practices.