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

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.