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

Abstract

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.

Graphical abstract

Keywords

SVM-based classification / personalized education / learning ability classification / system modeling

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Acknowledgments

This work was 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), and 2022 Guizhou Province Gold Course Construction Project.

Conflict of Interest

The authors declare that they have no conflict of interest related to the content of this paper.

Ethics Statements

The authors declare that their Institutional Ethics Committee confirmed that no ethical review was required for this study. Written informed consent for participation was not required because all participants’ data was anonymized before the statistical analyses were done.

Data Availability Statements

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

RIGHTS & PERMISSIONS

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