基于学生个人大数据的行为特征分析

Analysis of behavioral characteristics based on student's personal big data

高校各类教学管理业务系统记录着大学生日常学习和生活的行为数据,逐步形成规模较大,类型多样的学生个人大数据环境.该文从学生学籍信息、学习表现、校园生活三个维度进行分析,构建学生个人大数据行为分析模型,并对学生校园消费数据进行挖掘,探究学生饮食规律和消费水平.通过数据分析,得出以下特征:1)在校期间学生就餐总人次和早餐就餐率均呈递减趋势;2)大一新生早餐就餐时间早于全校早餐就餐高峰1 h;3)学生消费水平越稳定、饮食越规律、学习努力程度越高,学生学业表现水平越好;4)学生的学业成绩与正餐就餐率、早餐就餐率、就餐消费水平等变量有较强的相关性.

With the continuous improvement of the information construction of colleges and universities,the daily life and learning behaviors of college students are recorded and stored by major business systems,which gradually forming a large-scale,multi-type student personal big data environment.This paper mainly classifies and summarizes the students'data from the three aspects including student basic information,campus learning and campus life.It focuses on the feature extraction and index mining of students'campus consumption,curriculum and performance data,and constructs the student's personal big data behavior analysis model.Through data analysis,the following rules were found.1)The total number of students eating at school and the breakfast rate decrease year by year.2)Freshmen are one hour ahead of the“peak period”of breakfast meals for the whole group.3)The students'academic scores are highly correlated with the meal rate,breakfast meal rate and eating consumption level,and are less correlated with variables such as window selection stability,etc.4)The more regular the student's diet,the more stable the level of consumption,and the higher the level of learning effort,the better the student's academic performance.