[1]刘大莲,田英杰.可拓数据挖掘在学生成绩分析中的应用研究[J].智能系统学报,2022,17(4):707-713.[doi:10.11992/tis.202112020]
LIU Dalian,TIAN Yingjie.Application of extension data mining in student achievement analysis[J].CAAI Transactions on Intelligent Systems,2022,17(4):707-713.[doi:10.11992/tis.202112020]
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LIU Dalian,TIAN Yingjie.Application of extension data mining in student achievement analysis[J].CAAI Transactions on Intelligent Systems,2022,17(4):707-713.[doi:10.11992/tis.202112020]
可拓数据挖掘在学生成绩分析中的应用研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第4期
页码:
707-713
栏目:
学术论文—机器学习
出版日期:
2022-07-05
- Title:
- Application of extension data mining in student achievement analysis
- Keywords:
- extenics; data mining; classification; clustering; support vector machine; Pearson’s correlation coefficient; big data in education; analysis of student score
- 分类号:
- TP18
- 摘要:
- 为了充分利用教育大数据资源，促进教学改革良性发展，本文利用可拓支持向量机、 可拓k-均值聚类等多种可拓数据挖掘方法及皮尔逊相关系数，对高校学生数学课程的平时作业、期中和期末考试成绩等进行挖掘和分析，探索试卷设计的科学性，学生对知识点的掌握程度，以及哪些题目是影响学生成绩的主要因素，针对每个学生给出其该门课程日后学习的侧重点等。将不断发展的前沿科研方法应用于需要不断改革的教育教学中，同时也对长期沉睡的庞大的学生成绩数据加以充分利用，科研指导教学，教学反哺科研，起到很好的示范作用。
- Abstract:
- To make full use of the educational big data resources and promote the sound development of teaching reform, this paper applies several extension data mining methods, including an extenics support vector machine, an improved k-means algorithm based on extension distance, etc., and the Pearson’s correlation coefficient, to analyze the usual homework, midterm, and final examination results of a college students’ mathematics course, to explore the scientificity of test paper design, students’ mastery of knowledge points, and which topics are the main factors affecting students’ performance. Furthermore, some advance information is given to each student to tell them which point they should focus on in this course later. This paper applies the constantly developing, cutting-edge scientific research methods to the education and teaching that need constant reform and makes full use of the huge student achievement data that has been sleeping for a long time. It has played a good example of scientific research guiding teaching and teaching feeding scientific research.
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备注/Memo
收稿日期:2021-12-11。
基金项目:国家自然科学基金面上项目（72071049）；教育部人文社科规划基金项目（18YJAZH049）；北京联合大学教育教学研究与改革项目 ( JJ2021Y053).
作者简介:刘大莲，副教授，主要研究方向为最优化理论与方法、数据挖掘。发表学术论文18篇;田英杰，教授，博士生导师，中国科学院大学经济与管理学院副院长，主要研究方向为机器学习、大数据挖掘与最优化。出版中英文专/合著5 部，近5年发表学术论文50余篇
通讯作者:田英杰. E-mail：tyj@ucas.ac.cn
更新日期/Last Update:
1900-01-01