基于深度学习的高校贫困生精准资助模型的构建
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摘要
国家精准扶贫政策的提出,对高校贫困生资助提出了新要求。针对高校贫困生资助问题,确立精准资助方法和贫困等级认定分类模型非常重要。通过利用原有的贫困生数据和高校智慧校园建设中所产生包括消费、图书借阅、各大门禁、学生成绩、学生上网时长等数据,并基于深度学习的相关技术,构建了高校贫困生精准资助模型,为实现高校贫困生的精准资助创造有利条件。
As a part of the establishment of a smart campus, the campus card records the consumption,learning and living conditions of students in the school. The data generated by students in the process of surfing the internet and using the systems managed by various Departments is of great research value. The introduction of the national targeted poverty alleviation policy raises new requirements for student funding in colleges and universities. This paper analyzes the existing targeted funding methods and classification models of poverty level confirmation in colleges and universities. By using a large number of poverty-stricken data of students and a large amount of data generated in the construction of smart campuses, including data of consumption,book borrowing, all access control, student achievements, student online hours, we will build a targeted funding model in colleges and universities based on deep learning, accurately identify poverty-stricken students and classify them according to their level of poverty.
As a part of the establishment of a smart campus, the campus card records the consumption,learning and living conditions of students in the school. The data generated by students in the process of surfing the internet and using the systems managed by various Departments is of great research value. The introduction of the national targeted poverty alleviation policy raises new requirements for student funding in colleges and universities. This paper analyzes the existing targeted funding methods and classification models of poverty level confirmation in colleges and universities. By using a large number of poverty-stricken data of students and a large amount of data generated in the construction of smart campuses, including data of consumption,book borrowing, all access control, student achievements, student online hours, we will build a targeted funding model in colleges and universities based on deep learning, accurately identify poverty-stricken students and classify them according to their level of poverty.
引文
[1]郭昕.我国普通高校贫困生资助问题研究[D].武汉:华中师范大学,2013.
[2]英光辉.大数据在精准扶贫过程中的应用及实践创新[J].求实,2016(10):87-96.
[3]万喆.新形势下中国贫困新趋势和解决路径探究[J].国际经济评论,2016(06):47-62.
[4]李成飞.大数据背景下高校贫困生资助工作精准化研究[D].南京:南京邮电大4学,2017.
[5]杨胜志.基于大数据的大学生精准资助贫困等级研究[D].长春:东北师范大学,2018.
[6]Peter Harrington.机器学习实战[M].北京:人民邮电出版社,2013.
[2]英光辉.大数据在精准扶贫过程中的应用及实践创新[J].求实,2016(10):87-96.
[3]万喆.新形势下中国贫困新趋势和解决路径探究[J].国际经济评论,2016(06):47-62.
[4]李成飞.大数据背景下高校贫困生资助工作精准化研究[D].南京:南京邮电大4学,2017.
[5]杨胜志.基于大数据的大学生精准资助贫困等级研究[D].长春:东北师范大学,2018.
[6]Peter Harrington.机器学习实战[M].北京:人民邮电出版社,2013.