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 ...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.展开更多
This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configura...This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.展开更多
基金supported by the 2021 Higher Education Teaching Reform Research and Practice Project of SEAC(Grant No.221057)2021 Ministry of Education Industry−University Cooperation Collaborative Education Project(Grant No.202102646007)2022 Guizhou Province Gold Course Construction Project.
文摘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.
文摘This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.
