摘要
文章针对高校贫困生评定工作中存在的问题,将传统贫困生评定方法和数据挖掘相结合,提出了一种精准的高校贫困生评定方法。该方法使用K-means算法对校园一卡通学生消费数据进行数据挖掘分析,将分析结果和传统评价指标代入贫困生精准评价模型中,计算出最终评价结果。实验结果表明,与传统方法相比,该方法的评价结果更加精准合理,能够为我国高校贫困学生的评定提供重要依据,保证了贫困学生认定工作的公正性。
Aiming at the problems existing in the evaluation of poor students in colleges and universities,this paper combines the traditional evaluation method of poor students with data mining and proposes an accurate evaluation method of poor students in colleges and universities.The method uses the K-means algorithm to data mine and analyze the consumption data of campus one card students,substitutes the analysis results and traditional evaluation indexes into the accurate evaluation model of poor students,and calculates the final evaluation results.The experimental results show that compared with the traditional method,the evaluation results of this method are more accurate and reasonable,it can provide an important basis for the evaluation of poor students in colleges and universities in China and ensure the fairness of the identification of poor students.
作者
张旭华
景安琪
李欣
ZHANG Xuhua;JING Anqi;LI Xin(Shaanxi Energy Institute,Xianyang Shaanxi 712000)
出处
《软件》
2024年第6期43-45,共3页
Software
基金
陕西能源职业技术学院校级科研项目(2022KY11KJP)。
关键词
消费数据
数据挖掘
贫困生
评价模型
consumption data
data mining
poor students
evaluation model
