大数据环境下高校贫困生精准资助模式初探  

Research on the Targeted Poverty Reduction Model of the Needy Undergraduates in the Big Data Environment

作　　者：吴朝文[1] 代劲[2] 孙延楠 

机构地区：[1]电子科技大学马克思主义教育学院,四川成都611731 [2]重庆邮电大学软件工程学院,重庆400065

出　　处：《黑龙江高教研究》2016年第12期41-44,共4页Heilongjiang Researches on Higher Education

基　　金：国家社科基金西部项目"移动互联网时代高校思想政治教育路径创新研究"(编号:14XKS038)

摘　　要：在中央"精准扶贫"的总体部署下,实现高校贫困生的精准资助具有重要意义。现行贫困生认定政策采取定性和定量相结合的方式,但定性环节的民主评议具有较多不确定性,同时定量标准也缺乏客观依据。基于高校"智慧校园"的建设发展,为运用大数据技术开展贫困生资助工作创造基础条件。随着学生日常生活的数字化,大数据客观真实地反映学生的生活轨迹和行为特征,在现有贫困生认定情况的基础上,通过对学生消费行为的特征分析,实现生活状况的评价。配合学生资助管理部门,将大数据分析的结果应用于对贫困生资助体系的验证性评估和特殊困难学生群体的预警,实现对现行贫困生认定有效的补充,从而实现高校贫困生的精准资助。Targeted funding for needy undergraduates plays an important role in the central government＇s overall scheme of precision-targeted poverty alleviation. The current approach to identify poverty students is evaluating the situation of the students qualitatively and quantitatively. However,the voting session of the qualitative process is full of uncertainty and the qualitative standard is lack of objective basis. The Smart Campus program offers a basis data for the evaluation of the financial situation of a student by the big data technology. The big data relevant to the daily life of a student reflect his habitual behaviors and life traces,by which the consuming behavior of the student could be analyzed to evaluate the financial situation. It could also be used as an early warning of extremely impoverished students. This will be an effective supplement to the affirmation of the poor students in the work of a precision-targeted subsidization.

关 键 词：智慧校园 精准扶贫 大数据 贫困生资助 

分 类 号：G645[文化科学—高等教育学]