K-means聚类算法在高校学生成绩分析中的应用研究

Application research of K-means clustering algorithm in college students’ performance analysis

系统地介绍了K-means聚类算法的思想和基本流程,把某高校学生大学四年专业必修课成绩作为研究对象,利用K-means聚类算法进行深层次数据挖掘;通过聚类分析结果,挖掘出了学生各科成绩分布情况,以及每个科目的重要程度,并依据重要程度调整学时及师资,提高教学效果;帮助教师采取分层教学、个性化指导策略,实施“精准”教学;帮助学生调整投入时间和精力,提高学习效果和成绩。

This study systematically introduces the idea and basic flow of the K-means clustering algorithm, taking the results of a four-year professional compulsory course of a university student as a research object, using K-means clustering algorithm for deep data mining;Through the results of cluster analysis, the distribution of students' scores in various subjects and the importance of each subject are excavated, and the hours and teachers are adjusted according to the importance level to improve the teaching effect;It helps teachers adopt layered teaching, personalized guidance strategies, implement “precise” teaching;helps students adjust investment time and energy, improve learning results and achievements.