作　　者： ; ; ; ; ;

机构地区： 上海应用技术学院

出　　处： 《计算机应用研究》 2012年第6期2168-2171,共4页

摘　　要： 针对高校学生人数增多、学生信息量增大,原有的学生管理和评估模式已经不能满足需求的问题,研究了学生状态分析系统,实现了学生综合数据的采集,为辅导员与学生提供了交互的平台;针对K-均值聚类算法在全局优化中的不足,研究了基于粒子群的K-均值聚类算法对学生综合数据进行分析。通过与K-均值聚类算法、基于遗传算法的K-均值聚类算法和人工评估的结果比较可以看出,本系统对学生的状态评估更加全面和客观;同时系统提供了便于查询的视图信息反馈,帮助辅导员及时发现问题、提高工作效率。 With the number of students and their information increasing,the original student management and evaluation pattern could not meet demand in college.This paper researched a student status analysis system,which could collect the students＇ comprehensive data and provide platform to communicate between counsellor and students.In view of the deficiency of global search ability for K-means clustering algorithm,this paper proposed K-means algorithm based on particle swarm optimization to analyze the students＇ data.Compared with those of K-means algorithm,K-means algorithm based on genetic algorithm and artificial evaluation,the results show the evaluation obtained from the system is more comprehensive and objective.The system could also offer visual information convenient to query,which helped counsellor find sticking point timely and improve efficiency.

关 键 词： 学生管理 学生状态评估 粒子群优化算法 均值算法

领　　域： [自动化与计算机技术] [自动化与计算机技术]