云平台数据挖掘的学生行为分析管理系统  

Students behavior analysis and management system based on cloud platform data mining

作　　者：王颇[1] WANG Po(Office of General Committee and Administration,Xi’an Aeronautical Polytechnic Institute,Xi’an 710089,China)

机构地区：[1]西安航空职业技术学院党政办,西安710089

出　　处：《信息技术》2022年第2期36-40,47,共6页Information Technology

基　　金：西安航空职业技术学院2019年度科研计划项目(19XHSK-006)。

摘　　要：运用数据挖掘技术对智慧校园云平台的学生行为数据进行多维分析,对高校学生校内消费行为和访问图书馆行为进行了分析,并根据智慧校园云平台学生数据的初步分析情况,将学生聚类分为正常活跃度群体、高活跃度群体、低活跃度群体、刻苦学习群体和体质较弱群体五大类。研究显示,学生行为在早午晚餐的就餐人数、就餐高峰和持续时间上均有较大的差异;6月和12月是学生群体进入图书馆学习的高峰期,且大部分学生选择在中午和晚上进入图书馆;高活跃度群体、低活跃度群体、刻苦学习群体和体质较弱群体是行为分析数据异常的群体,需要对这四类群体加以关注。Data mining technology is used to conduct multidimensional analysis of students behavior data on cloud platform of intelligent campus,and analyzes consumer behavior and library behavior of students.Then,based on the preliminary analysis of the students data of intelligent campus cloud platform,student clustering can be divided into five categories,including normal active groups,high activity and low activity,hard working and weak physique group.Research results show that there are huge differences existed in the number of people eating breakfast,lunch and dinner,and the differences between peak time and the duration of dinning is huge.June and December are the peak months for students to study in the library,and most of them choose to enter the library at noon and evening.High activity group,low activity group,hard working group and weak body group are groups with abnormal behavior analysis data,which should be paid more attention.

关 键 词：智慧校园云平台 数据挖掘 高校学生管理 行为分析 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]