工业技术与职业教育2025，Vol.23Issue(3)：72-76,5.
基于K-means算法的学前教育学生成绩聚类分析
Clustering Analysis of Preschool Education Students'Academic Performance Based on the K-means Algorithm
摘要
Abstract
Existing methods for analyzing student performance often rely on single-dimensional grade data,making it difficult to reflect students,diverse learning outcomes.This study,from a macro perspective of course types,applies the K-means algorithm to conduct a clustering analysis of students,average scores across three types of courses.The clustering results reveal significant differences in students,performance across these three course categories.Based on the characteristics of each cluster,targeted suggestions for teaching improvement are proposed.Additionally,specific recommendations are provided from the perspectives of students,teachers and educational administrators:students should actively engage in practical and operational tasks to enhance their comprehensive abilities;teachers should adopt a learner-centered approach and design differentiated teaching tasks;educational administrators need to optimize course offerings and provide precise resource support based on the learning characteristics of student groups,as well as revise talent development plans to ensure that course designs align with students,learning needs.关键词
聚类分析/课程/大数据技术Key words
clustering analysis/courses/Big Data technology分类
教育学引用本文复制引用
方琼..基于K-means算法的学前教育学生成绩聚类分析[J].工业技术与职业教育,2025,23(3):72-76,5.基金项目
2021年校级课题"基于WIFI的一种改进的WKNN匹配算法研究"(项目编号:HKYZXYB-2021-17),主持人方琼 （项目编号:HKYZXYB-2021-17）
2016年国家开放大学"基于学生话语权的开放教育翻转课堂教学模式研究与实践"(项目编号:G16A1625Q),主持人费晓蕾. （项目编号:G16A1625Q）
