摘要
针对教育大数据背景下高校学生管理面临的问题,提出了一种高校学生学业预警算法,利用现有高校数字校园建设成果,挖掘潜在的教育数据.采用Kendall相关性分析方法选择用于预测的特征数据,选择相关系数较高的8个特征数据作为BP神经网络的输入,采用相关性分析结果改进GA-BP算法,综合考虑各项因素实现学业情况的预测.经试验,该学业预警算法的预测准确率可以达到90%以上.
Aiming at the problems faced by college student management in the context of educational big data,this study proposes an academic early warning algorithm for college students.It mines potential education data with the results of digital campus construction in colleges and universities.Eight characteristic data with higher correlation coefficients selected by the Kendall correlation analysis are taken as the input for the BP neural network,and the relevant results are applied to improving the GA-BP algorithm.Thus,the academic situation is predicted by taking into account various factors.The tests demonstrate that the prediction accuracy of the proposed algorithm can reach more than 90%.
作者
姜绍萍
JIANG Shao-Ping(Department of Information and Control Engineering,Yantai Automobile Engineering Professional College,Yantai 265500,China)
出处
《计算机系统应用》
2021年第4期199-203,共5页
Computer Systems & Applications
关键词
相关性分析
GA-BP
学业预警
教育数据
correlation analysis
GA-BP
academic early warning
education data
