摘要
大数据时代,教育数据治理面临更为复杂的困境,精准资助是国家教育扶贫的核心任务之一,鉴于目前高校难以有效进行贫困生精准识别,文章提出一种基于大数据技术的高校精准资助模型。通过梳理学生在校的全量数据,设计提取学生基本信息、消费数据等共计23类数据对学生的经济情况进行建模评估,通过数据采集、数据处理、数据集中展现,精准识别贫困生与非贫困生。该模型优化引用机器学习算法对模型进行训练,结合学工大数据对结果进行评价,能及时发现"隐性贫困",得到一个有效的贫困生识别方法。
In the era of big data, education data governance is faced with more complex difficulties, and precise subsidy is the core task of education poverty alleviation. In view of the difficulty of accurate identification of poor students’in colleges and universities, this paper proposes a precise subsidy model of colleges and universities based on big data technology. By combing the total data of students’in school, we design and extract a total of 23 types of data, such as students’basic information and consumption data, to model and evaluate the economic situation of students, and accurately identify poor students’and non-poor students’through data collection, data processing and data centralized display. The model uses machine learning algorithm to train the model optimally, and evaluates the results with the big data of Nanjing Forestry University. It can find out the "hidden poverty "in time and get an effective method to identify the poor students.
作者
景璐璐
陈天宇
顾炜江
潘卿
JING Lulu;CHEN Tianyu;GU Weijiang;PAN Qing(Office of Cyberspace Aff airs,Nanjing Forestry University,Nanjing Jiangsu 210037;China Mobile(Suzhou)Software Technology Co.,Ltd.,Suzhou Jiangsu 215000)
出处
《软件》
2021年第3期90-93,共4页
Software
基金
2018年江苏省教育信息化课题(20180008)
2018年南京林业大学党建与思想政治教育课题重点资助项目(163140051)
江苏省现代教育技术研究所智慧校园专题项目(2019-R-75631)。
关键词
教育数据治理
精准资助模型
机器学习算法
education data governance
precision funding model
machine learning algorithm
