利用基本信息和行为数据发现高校贫困学生  

Identifying Poor Students in Universities by Using Basic Information and Behavioral Data

作　　者：聂敏 张杨 邓辉 王伟 夏虎 周涛 NIE Min;ZHANG Yang;DENG Hui;WANG Wei;XIA Hu;ZHOU Tao(Big Data Research Center,University of Electronic Science and Technology of China Chengdu 611731)

机构地区：[1]电子科技大学大数据研究中心,成都611731

出　　处：《电子科技大学学报》2020年第5期795-800,共6页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金(11975071,61433014)。

摘　　要：高校学生的扶贫助困工作一直是教育各界关注的重点,如何利用有效的大数据分析手段减轻评审工作量和公平化评审流程,从而实现高校精准扶贫的目标,是一项值得深入研究的问题。该文以高校学生行为数据为基础,结合高校数据的时序性特点,抽取学生基本信息和行为数据的多维特征,提出基于深度学习理论的CW-LSTM算法进行预测。最后使用真实数据对模型进行验证,结果显示,该方法优于朴素贝叶斯算法和决策树算法。The poverty alleviation work for college students has always been the focus of attention in education.How to use effective big data analysis methods to reduce the workload of review and fair review process and achieve the goal of targeted poverty alleviation in colleges and universities is a question worthy of further study.Based on the behavioral data of college students,this paper combines the time-series characteristics of college data,extracts the basic information and multi-dimensional features of behavioral data,and proposes a clockwork long short-term memory(CW-LSTM)algorithm based on deep learning theory for prediction.Finally,the model is verified using real data,and the results show that our method is better than the Naive Bayes algorithm and decision tree algorithm.

关 键 词：大数据 数据挖掘 家庭贫困学生 学生行为数据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]