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

Research Article

Optimization of Instructional Management Strategies in Universities Based on Genetic Algorithms and Transfer Learning

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  • @INPROCEEDINGS{10.4108/eai.24-11-2023.2343584,
        author={Gang  Lei and Duanyang  Feng and Wennan  Wang and Lili  Wang and Ziwen  Sun},
        title={Optimization of Instructional Management Strategies in Universities Based on Genetic Algorithms and Transfer Learning},
        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={genetic algorithm transfer learning university teaching management optimization strategy},
        doi={10.4108/eai.24-11-2023.2343584}
    }
    
  • Gang Lei
    Duanyang Feng
    Wennan Wang
    Lili Wang
    Ziwen Sun
    Year: 2024
    Optimization of Instructional Management Strategies in Universities Based on Genetic Algorithms and Transfer Learning
    ITEI
    EAI
    DOI: 10.4108/eai.24-11-2023.2343584
Gang Lei1, Duanyang Feng2, Wennan Wang3,*, Lili Wang2, Ziwen Sun2
  • 1: Guangdong University of Science and Technology
  • 2: City University of Macau
  • 3: Xiamen University
*Contact email: wwwennan@xmu.edu.cn

Abstract

With the continuous development in the field of education, the optimization of teaching management strategies in colleges and universities has become a hot topic of research and practice. This study specifically proposes an optimization method based on genetic algorithm and transfer learning for teaching management strategies in universities. We selected developing colleges and universities in a province in mainland China as the research object to ensure the wide applicability of the study. After a series of experimental comparisons, the method shows excellent performance in key evaluation metrics such as classroom interaction, students' learning autonomy, and long-term knowledge retention, which clearly outperforms existing methods. In addition, the method provides a new direction for thinking and practicing for teaching management in universities and provides a useful reference for future educational research.