Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–26, 2023, Zhengzhou, China

Research Article

Visualized Teaching Management System Based on Model Library and Knowledge Base

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  • @INPROCEEDINGS{10.4108/eai.24-11-2023.2343579,
        author={Lin  Wang},
        title={Visualized Teaching Management System Based on Model Library and Knowledge Base},
        proceedings={Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24--26, 2023, Zhengzhou, China},
        publisher={EAI},
        proceedings_a={ITEI},
        year={2024},
        month={4},
        keywords={model library knowledge base visualization management system},
        doi={10.4108/eai.24-11-2023.2343579}
    }
    
  • Lin Wang
    Year: 2024
    Visualized Teaching Management System Based on Model Library and Knowledge Base
    ITEI
    EAI
    DOI: 10.4108/eai.24-11-2023.2343579
Lin Wang1,*
  • 1: Shandong University of Finance and Economics
*Contact email: tg667788@xzcstudio.com

Abstract

The paper discusses a visualization-based teaching management system that is founded on a model repository and a knowledge base. This system is designed to enhance stu-dent learning outcomes and teaching efficiency by integrating a variety of educational resources to provide real-time diagnostics, personalized education, and instructional feedback. The objective of the research is to develop a new teaching management system that utilizes model repositories and knowledge bases for the visual management of educational resources, thereby offering a more comprehensive educational expe-rience to both teachers and students. The significance of this research lies in its ability to improve the accessibility and manageability of educational resources, aiding teachers in better managing these resources to meet the individualized learning needs of students more effectively. This paper is primarily based on artificial intelligence and data mining technologies to develop this visual educational management system. It uses model repositories and knowledge bases to integrate educational resources and has designed adaptive learning and real-time diagnostic modules to provide a better learning expe-rience and instructional feedback. In the development process, it is first necessary to analyze and categorize teaching requirements to provide different types of students with various learning resources.