基于校园一卡通和云课堂数据的消费与学习行为分析

发布时间：2018-05-07 04:18

本文选题：消费行为 + 学习行为　；参考：《华中师范大学》2017年硕士论文

【摘要】：现如今,校园一卡通系统已在高校信息化建设不断发展的过程中普及应用,一卡通为广大师生员工的生活、学习带来了极大方便的同时,也使学校的管理工作更加简便、快捷、高效。与此同时,校园一卡通系统在运行的过程中产生了大量数据,这些数据记录了师生员工的日常学习和生活信息。云课堂学习平台是我校云端一体化学习平台,广泛应用于我校各个本科院系及研究生院,其主要设计目标是供学生网络学习并提供课程管理,更好的满足教师的教学和学生的学习,以及师生间的教学互动。同时,学生的学习行为数据在云课堂学习平台后台被记录。那么如何利用这些数据、从这些数据中发现隐藏的规律和信息,以对学校的管理工作、教育教学工作提供参考和帮助,值得进一步探究。自20世纪90年代以来,数据挖掘技术迅速发展,成为近年来新兴的热门学科。数据挖掘是在大型数据存储库中自动发现有用信息的过程,它要处理的问题,就是在庞大的数据库中找出有价值的隐藏事件,并加以分析,从而得出对机构、企业等有用的信息。目前数据挖掘技术已大量应用在零售业、制造业、财务金融保险、通讯及医疗服务行业。本文借助数据挖掘技术和Weka数据挖掘平台,用K-means聚类算法对学生消费行为数据做聚类分析,并借助Excel工具对消费行为数据进行多维度统计分析,以更加全面的了解学生的消费情况。同时,用Apriori关联规则算法对学生学习行为数据、消费行为数据做关联规则挖掘,以了解学生的学习行为,以及消费行为与学习行为之间的关联性。通过本文研究工作,一方面将学生在校内的日常消费行为划分类别,并从学校服务、管理角度提出建议、提供参考;另一方面挖掘了“成绩”、“在线学习时长”、“年消费额”三个属性间的关联规则,为教育教学工作的改善提供参考。
[Abstract]:Nowadays, the campus one-card system has been popularized and applied in the process of the continuous development of the information construction in colleges and universities. The one-card card has brought great convenience to the life and study of the teachers and students, and at the same time, it has also made the management of the school more convenient and faster. High efficiency. At the same time, a large number of data are generated in the course of running the campus card system, which records the daily learning and life information of teachers and students. The cloud classroom learning platform is an integrated learning platform of our school, which is widely used in all undergraduate departments and graduate schools of our university. Its main design goal is to provide students with network learning and curriculum management. Better meet the teacher's teaching and students' learning, as well as the teaching interaction between teachers and students. At the same time, the data of students' learning behavior are recorded in the background of cloud classroom learning platform. So how to use these data to find hidden laws and information from these data in order to provide reference and help for school management and education and teaching work is worth further exploration. Since 1990's, data mining technology has developed rapidly and become a hot subject in recent years. Data mining is the process of automatically discovering useful information in large data repositories. The problem it has to deal with is to find valuable hidden events in large databases, and analyze them, and then draw a conclusion on the mechanism. Useful information such as business. At present, data mining technology has been widely used in retail, manufacturing, finance, finance, insurance, communications and medical services. In this paper, with the help of data mining technology and Weka data mining platform, we use K-means clustering algorithm to cluster students' consumer behavior data, and use Excel tools to carry out multi-dimensional statistical analysis of consumer behavior data. In order to a more comprehensive understanding of the student consumption situation. At the same time, the Apriori association rules algorithm is used to mine the learning behavior data and the consumer behavior data to understand the students' learning behavior and the correlation between the consumer behavior and the learning behavior. Through the research work of this paper, on the one hand, it classifies the students' daily consumption behavior in the school, and puts forward suggestions from the school service and management angle to provide the reference; on the other hand, it excavates the "achievement", "the online study time", The association rules between the three attributes of "annual consumption" provide a reference for the improvement of education and teaching.
【学位授予单位】：华中师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：G434

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