吉林大学学报(信息科学版) ›› 2023, Vol. 41 ›› Issue (5): 903-907.

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基于支持向量机的学位预警方法研究

王 娜a , 李劲松a , 潘子尧b , 姚明海a   

  1. 渤海大学 a. 信息科学与技术学院; b. 数学科学学院, 辽宁 锦州 121013
  • 收稿日期:2022-11-11 出版日期:2023-10-09 发布日期:2023-10-10
  • 作者简介:王娜( 1981— ), 女, 辽宁盘锦人, 渤海大学讲师, 主要从事教育理论与实践研究, ( Tel) 86-416-3400316 ( E-mail) 58134340@ qq. com; 姚明海(1980— ), 男(锡伯族), 辽宁铁岭人, 渤海大学副教授, 博士, 主要从事模式识别与智能 计算研究, (Tel)86-416-3400316(E-mail)yao_ming_hai@ 163. com。
  • 基金资助:
    辽宁省社会科学规划基金资助项目(L22BTJ002) 

Research on Early Warning of Degree Based on Support Vector Machine

WANG Na a , LI Jinsong a , PAN Ziyao b , YAO Minghai a   

  1. a. College of Information Science and Technology; b. College of Mathematical Science, Bohai University, Jinzhou 121013, China
  • Received:2022-11-11 Online:2023-10-09 Published:2023-10-10

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。

关键词: 教育数据挖掘, 学位预警, 成绩预测, 支持向量机 

Abstract:

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.

Key words: education data mining, degree early warning, performance prediction, support vector machines (SVM)

中图分类号: 

  • TP183