Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15–17, 2023, Nanjing, China

Research Article

Research on the Application of Data Mining Techniques in the Evaluation of Marketing Education in Market-Oriented Programs

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  • @INPROCEEDINGS{10.4108/eai.15-12-2023.2345356,
        author={Jing  Shi},
        title={Research on the Application of Data Mining Techniques in the Evaluation of Marketing Education in Market-Oriented Programs},
        proceedings={Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15--17, 2023, Nanjing, China},
        publisher={EAI},
        proceedings_a={PMBDA},
        year={2024},
        month={5},
        keywords={data mining; teaching evaluation; marketing education},
        doi={10.4108/eai.15-12-2023.2345356}
    }
    
  • Jing Shi
    Year: 2024
    Research on the Application of Data Mining Techniques in the Evaluation of Marketing Education in Market-Oriented Programs
    PMBDA
    EAI
    DOI: 10.4108/eai.15-12-2023.2345356
Jing Shi1,*
  • 1: Shandong Institute of Commerce and Technology
*Contact email: stone1209@163.com

Abstract

The accumulation of teaching evaluation data provides the possibility for the application of data mining techniques. In this study, we focus on marketing education and construct data mining-based models for evaluating teaching effectiveness and analyzing student learning behavior. First, we explain the methods and steps of data mining techniques, as well as the data and indicator system involved in teaching evaluation. Then, we use linear regression algorithm to establish a teaching effectiveness evaluation model and apply KNN clustering algorithm to develop a student learning behavior model. The predictive performance of the models is validated using synthetic data. Additionally, specific case analyses are designed to compare the differences with traditional evaluation methods, demonstrating the scientific validity of the research approach. Overall, this study provides a new perspective and method for improving the quality of teaching evaluation using data mining, with both theoretical value and practical application prospects. However, the models can still be further optimized and expanded for broader applicability.