基于大数据分析的高校贫困生精准资助策略研究

ACCURATE FINANCIAL AID STRATEGY FOR POOR COLLEGE STUDENTS BASED ON BIG DATA ANALYSIS

精准扶贫成为国家当前一项重要工作。对高校而言,如何分辨真正的贫困生是难点。根据现有的贫困生名单,通过分析校园卡消费数据将学生分成两类统计消费特征,分析出消费金额和消费次数之间的相关性,研究贫困生的消费观点和预测消费特征。采用CHAID算法,找出最佳分组变量和分组点,设计判别贫困生的模型。实验结果表明,该模型能较好判别贫困生,找出疑似非贫困生以及具有过度贫困消费特征的学生,为学校资助贫困生提供数据参考。

Targeted poverty alleviation has become an important task of our country at present.For colleges and universities,how to distinguish the real poor students is a difficult problem.According to the existing list of poor students,through the analysis of campus card consumption data,students are divided into two categories of statistical consumption characteristics.This paper analyzes the correlation between consumption amount and consumption frequency,and studies the consumption viewpoints and prediction consumption characteristics of poor students.The best grouping variables and grouping points were found out by using CHAID algorithm,and a model for identifying impoverished students was designed.The experimental results show that the model can better distinguish the poor students,and find out the suspected non-poor students,and the students with the characteristics of excessive poverty consumption.It provides data reference for schools to subsidize the poor students.