基于随机森林和K-Means算法的高校学生评教指标的应用研究

Research on the Application of Teaching Evaluation Indicators for College Students Based on Random Forest and K-Means Algorithm

本文旨在探讨随机森林和K-means算法在高校学生评教体系中的应用及其有效性。首先,通过构建随机森林模型对评教数据进行拟合,分析模型的均方误差和拟合优度,验证其预测能力。进一步利用随机森林的特征重要性评估功能,筛选出对评教结果影响较大的指标,为优化评教体系提供科学依据。同时,对评教指标进行相关性分析,揭示指标间的相互关系。其次,采用K-means算法对评教数据进行聚类分析,通过轮廓系数确定最佳聚类数,并成功将数据划分为三个具有明显差异的聚类。聚类结果揭示了不同教师在教学理念、风格和要求上的多元性,为教学改进和提升提供了参考依据。本文的方法论和结果对优化高校学生评教体系、提升教学质量具有重要意义。

The purpose of this paper is to explore the application and effectiveness of random forest and K-means algorithm in the evaluation system of college students. Firstly, a random forest model was constructed to fit the evaluation data, and the mean square error and goodness-of-fit of the model were analyzed to verify its prediction ability. Furthermore, the feature importance evaluation function of random forest was used to screen out the indicators that had a great impact on the evaluation results, so as to provide a scientific basis for optimizing the evaluation system. At the same time, the correlation analysis of the evaluation indicators was carried out to reveal the correlation between the indicators. Secondly, the K-means algorithm was used to analyze the clustering of the evaluation data, and the optimal number of clusters was determined by the contour coefficient, and the data were successfully divided into three clusters with obvious differences. The clustering results revealed the diversity of teaching concepts, styles and requirements of different teachers, and provided a reference for teaching improvement and promotion. The methodology and results of this paper are of great significance for optimizing the student evaluation system and improving the teaching quality of colleges and universities.