[1]马婉晴,冯军,袁园.基于课程关联的高校学生成绩预测模型研究[J].浙江科技学院学报,2024,(03):205-217.[doi:10.3969/j.issn.1671-8798.2024.03.003 ]
 MA Wanqing,FENG Jun,YUAN Yuan.Study on performance prediction model of university students based on course association[J].,2024,(03):205-217.[doi:10.3969/j.issn.1671-8798.2024.03.003 ]
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基于课程关联的高校学生成绩预测模型研究(/HTML)
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《浙江科技学院学报》[ISSN:1001-3733/CN:61-1062/R]

卷:
期数:
2024年03期
页码:
205-217
栏目:
出版日期:
2024-06-28

文章信息/Info

Title:
Study on performance prediction model of university students based on course association
文章编号:
1671-8798(2024)03-0205-13
作者:
马婉晴冯军袁园
(浙江科技学院 经济与管理学院,杭州 310023)
Author(s):
MA Wanqing FENG Jun YUAN Yuan
(School of Economics and Management, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China)
关键词:
成绩预测 课程关联 机器学习 预测模型
分类号:
TP312.8; G424.7
DOI:
10.3969/j.issn.1671-8798.2024.03.003
文献标志码:
A
摘要:
【目的】为监测学习状况、管理学生成绩和提高教学质量,提出一种基于课程关联的学生成绩预测模型(students performance prediction using course association,SPCA)。【方法】以学生综合数据库为基础,挖掘课程间的关联程度,利用大数据技术对高校学生成绩进行分析和预测。选取某校2018—2020级工业工程专业学生的29门课程成绩,首先利用自组织映射网络(self-organizing map,SOM)算法对课程进行聚类,分为数学计算、通识与专业基础、实践应用三类; 然后利用先验算法(apriori algorithm,Apriori)挖掘课程间的关联规则; 最后采用决策树算法,利用处于同一类并且在关联规则中的前置课程成绩,对后置课程成绩进行预测。【结果】预测模型最终精确率为90.2%,准确率为88.9%,是预测学生成绩的较为有效的模型。【结论】本预测模型能优化课程安排,并帮助学生规划学习计划,对提高教学质量和改进教学管理具有一定的参考意义。

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备注/Memo

备注/Memo:
收稿日期:2023-10-10
基金项目:辽宁省社会科学规划基金项目(L21CGL005); 浙江科技学院教学研究与改革重大项目(2022-jg06)
通信作者:冯 军(1963— ),男,浙江省海盐人,研究员,主要从事教育和教学管理的相关研究。E-mail:101006@zust.edu.cn。
更新日期/Last Update: 2024-06-28