Proceedings of the 2nd International Conference on Internet Technology and Educational Informatization, ITEI 2022, December 23-25, 2022, Harbin, China

Research Article

Management of Ideological and Political Education of College Based on Data Mining

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  • @INPROCEEDINGS{10.4108/eai.23-12-2022.2329086,
        author={Ge  Chen and Kangsheng  Hu and Chaofeng  Huang},
        title={Management of Ideological and Political Education of College Based on Data Mining},
        proceedings={Proceedings of the 2nd International Conference on Internet Technology and Educational Informatization, ITEI 2022, December 23-25, 2022, Harbin, China},
        publisher={EAI},
        proceedings_a={ITEI},
        year={2023},
        month={6},
        keywords={data mining; educational management; k-means algorithm},
        doi={10.4108/eai.23-12-2022.2329086}
    }
    
  • Ge Chen
    Kangsheng Hu
    Chaofeng Huang
    Year: 2023
    Management of Ideological and Political Education of College Based on Data Mining
    ITEI
    EAI
    DOI: 10.4108/eai.23-12-2022.2329086
Ge Chen1,*, Kangsheng Hu2, Chaofeng Huang3
  • 1: National University of Defense Technologies
  • 2: Shijiazhuang Campus of Army Engineer University
  • 3: National University of Defense Technology
*Contact email: Earrychen@163.com

Abstract

The evaluation method of work assessment quantification table is suitable for vertically measuring the mastery degree of ideological and political education administrators to the students they manage. The information contained in this method is obviously insufficient, and there are some defects in the objectivity and accuracy of the evaluation results. It is invalid and inappropriate to evaluate ideological and political educators according to traditional analysis methods. This paper takes the management of ideological and political education in colleges and universities as the research object, combines data mining technology, adopts the idea of cluster analysis, and uses k-means algorithm to automatically analyze and mine a large number of evaluation data in the "work evaluation quantification table", and verifies it with real data, Experiments show that this method can effectively overcome the shortcomings of traditional analysis methods.