Analysis of behavioral characteristics based on student's personal big data
SHU Jiangbo1,2， GE Xiong1， PENG Liyuan1， HU Qianqian1， LIU Sanya1,2
1.National Engineering Center for E-learning， Central China Normal University， Wuhan 430079， China；2.National Engineering Laboratory for Educational Big Data， Central China Normal University，Wuhan 430079， China
Abstract：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.
舒江波,葛 雄,彭利园,胡茜茜,刘三女牙,. 基于学生个人大数据的行为特征分析[J]. 华中师范大学学报(自然科学版), 2020, 54(6): 927-934.
SHU Jiangbo,GE Xiong,PENG Liyuan,HU Qianqian,LIU Sanya,. Analysis of behavioral characteristics based on student's personal big data. journal1, 2020, 54(6): 927-934.
2020, Vol. 54 
