基于支持向量机的学位预警方法研究  

Research on Early Warning of Degree Based on Support Vector Machine

作　　者：王娜 李劲松 潘子尧 姚明海[1] WANG Na;LI Jinsong;PAN Ziyao;YAO Minghai(College of Information Science and Technology,Bohai University,Jinzhou 121013,China;College of Mathematical Science,Bohai University,Jinzhou 121013,China)

机构地区：[1]渤海大学信息科学与技术学院,辽宁锦州121013 [2]渤海大学数学科学学院,辽宁锦州121013

出　　处：《吉林大学学报（信息科学版）》2023年第5期903-907,共5页Journal of Jilin University（Information Science Edition）

基　　金：辽宁省社会科学规划基金资助项目(L22BTJ002)。

摘　　要：针对现有高校毕业生学位预测研究多是集中于构建成绩预测模型,忽略了学位预警工作的重要性,为此,提出基于支持向量机的学位预警模型。通过应用某高校2018级广播电视编导、汉语言文学、化学、会计学和数学与应用数学5个专业实际数据对其进行了大量实验验证。结果表明,构建的预警模型具有良好的准确度和实用性,可以成为提升教学质量的重要组成部分,为教师改进教学方案,学生改变学习方法提供参考。Most of the existing research on degree prediction in colleges and universities focuses on the construction of performance prediction models,ignoring the importance of degree early warning.Therefore,a degree early warning model based on support vector machine is proposed.A large number of experiments are carried out on the real data of 5 majors,including Broadcast and Television Directing Major,Chinese Language and Literature Major,Chemistry Major,Accounting Major and Mathematics and Applied Mathematics Major,in a university of 2018.The experimental results show that the constructed early warning model has good accuracy and practicality,which can become an important part of improving the teaching quality,and provide practical reference support for teachers to improve the teaching plan and for students to change their learning habits.

关 键 词：教育数据挖掘 学位预警 成绩预测 支持向量机 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] G420[自动化与计算机技术—控制科学与工程]