sis 18: e41

Research Article

Individual recommendation method of college physical education resources based on cognitive diagnosis model

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  • @ARTICLE{10.4108/eai.10-2-2022.173379,
        author={Hongming Guo and Xiaochun Cheng},
        title={Individual recommendation method of college physical education resources based on cognitive diagnosis model},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={2},
        keywords={Cognitive diagnosis model, College physical education, Teaching resources, Personalized recommendation, Probability matrix decomposition, Knowledge mastery attribute},
        doi={10.4108/eai.10-2-2022.173379}
    }
    
  • Hongming Guo
    Xiaochun Cheng
    Year: 2022
    Individual recommendation method of college physical education resources based on cognitive diagnosis model
    SIS
    EAI
    DOI: 10.4108/eai.10-2-2022.173379
Hongming Guo1, Xiaochun Cheng2,*
  • 1: Department of Sports, Wuxi Vocational Institute of Commerce, Wuxi Jiangsu 214000, China
  • 2: Department of Computer Science, Middlesex University, London NW4 4BT, United Kingdom
*Contact email: cheng@mdx.ac.uk

Abstract

In order to improve the safety of college physical education resources recommendation and reduce the test overlap rate and resource exposure rate, a personalized recommendation method of college P.E. teaching resources based on cognitive diagnosis model is proposed. A cognitive diagnosis model based on multi-level attribute score is designed to model students' resource mastery level according to existing answers and the relevance of knowledge points. The knowledge mastery attribute model of the tested students is used for probability matrix decomposition to predict the students' answers, and make corresponding resource recommendations according to the score prediction and resource difficulty. Experiments show that resource exposure value of the method in this paper is lower than 1, and its security is high. Regarding the experiment of overlapping indicators, the value of the test overlap rate of the method in this paper is always lower than 0.01, and the recommended resources are more accurate. The F1 value of the method in this paper is up to 0.98, and the deviation of resource recommendation is small. And the real-time performance is high after the method is applied, and the phenomenon of cold start will not occur.