[1]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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Application of extension data mining in student achievement analysis

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 4 Page number: 707-713 Column: 学术论文—机器学习 Public date: 2022-07-05
Title:
Application of extension data mining in student achievement analysis
Author(s):
LIU Dalian1; 2;  TIAN Yingjie3
1. Institute of Mathematics and Physics, Beijing Union University, Beijing 100101, China;
2. Institute of Fundamental and Interdisciplinary Sciences, Beijing Union University, Beijing 100101, China;
3. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China
Keywords:
extenics; data mining; classification; clustering; support vector machine; Pearson’s correlation coefficient; big data in education; analysis of student score
CLC:
TP18
DOI:
10.11992/tis.202112020
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.
References:
[1] 徐承俊, 朱国宾. 数据挖掘在全国计算机等级考试(NCRE)成绩分析中的研究及应用[J]. 计算机应用与软件, 2020, 37(8): 64–67,73
XU Chengjun, ZHU Guobin. Research and application of data mining in national computer rank examination (NCRE) achievement analysis[J]. Computer applications and software, 2020, 37(8): 64–67,73
[2] 郭鹏, 蔡骋. 基于聚类和关联算法的学生成绩挖掘与分析[J]. 计算机工程与应用, 2019, 55(17): 169–179
GUO Peng, CAI Cheng. Data mining and analysis of students’ score based on clustering and association algorithm[J]. Computer engineering and applications, 2019, 55(17): 169–179
[3] 唐笑林. 数据挖掘技术的研究和应用[J]. 华东理工大学学报(自然科学版), 2008, 34(2): 290–295
TANG Xiaolin. Application and research of data mining[J]. Journal of east China University of Science and Technology (natural science edition), 2008, 34(2): 290–295
[4] 张树滑. 基于ID3算法的大学生成绩数据挖掘与体能分析系统设计[J]. 现代电子技术, 2019, 42(5): 104–106,110
ZHANG Shuhua. Design of sports achievement data mining and physical fitness analysis system based on ID3 algorithm[J]. Modern electronics technique, 2019, 42(5): 104–106,110
[5] 王小根, 陈瑶瑶. 多模态数据下混合协作学习者情感投入分析[J]. 电化教育研究, 2022, 43(2): 42–48,79
WANG Xiaogen, CHEN Yaoyao. Analysis of blended collaborative learners’ emotional engagement based on multimodal data[J]. E-education research, 2022, 43(2): 42–48,79
[6] 沈苗, 来天平, 王素美, 等. 北京大学课程推荐引擎的设计和实现[J]. 智能系统学报, 2015, 10(3): 369–375
SHEN Miao, LAI Tianping, WANG Sumei, et al. Design and implementation of the course recommendation engine in Peking University[J]. CAAI transactions on intelligent systems, 2015, 10(3): 369–375
[7] 丁智斌, 袁方, 董贺伟. 数据挖掘在高校学生学习成绩分析中的应用[J]. 计算机工程与设计, 2006, 27(4): 590–592
DING Zhibin, YUAN Fang, DONG Hewei. Application of data mining to analysis of university students’ grades[J]. Computer engineering and design, 2006, 27(4): 590–592
[8] 喻铁朔, 李霞, 甘琤. 基于学生成绩回归预测的多模型适用性对比研究[J]. 中国教育信息化, 2020(17): 23–28
[9] 钟文精, 焦中明, 蔡乐. 基于K-Means算法的学生成绩聚类分析[J]. 教育信息技术, 2021(5): 56–58
[10] 蔡文, 杨春燕, 何斌. 可拓逻辑初步[M]. 北京: 科学出版社, 2003.
[11] CAI Wen, YANG Chunyan, LIN Weiehu. Extension engineering methods[M]. Beijing: Science Press, 2003
[12] 蔡文. 可拓集合和不相容问题[J]. 科学探索学报, 1983(1): 83–97
CAI Wen. Extenics and incompatibility[J]. Journal of scientific exploration, 1983(1): 83–97
[13] 李文军, 杨春燕, 汤龙, 等. 可拓学中相关关系的变换方法研究[J]. 智能系统学报, 2019, 14(4): 619–626
LI Wenjun, YANG Chunyan, TANG Long, et al. Research on the transformation method for the correlation relation in extenics[J]. CAAI transactions on intelligent systems, 2019, 14(4): 619–626
[14] 杨春燕, 李卫华, 汤龙, 等. 基于可拓学和HowNet的策略生成系统研究进展[J]. 智能系统学报, 2015, 10(6): 823–830
YANG Chunyan, LI Weihua, TANG Long, et al. Strategy-generating system based on extenics and HowNet[J]. CAAI transactions on intelligent systems, 2015, 10(6): 823–830
[15] 王丽萍, 叶季平, 苏学灵, 等. 基于可拓学理论的防洪调度方案评价研究与应用[J]. 水利学报, 2009, 40(12): 1425–1434
WANG Liping, YE Jiping, SU Xueling, et al. Evaluation of flood control operation program based on extenics theory and its application[J]. Journal of hydraulic engineering, 2009, 40(12): 1425–1434
[16] 杨春燕, 李兴森. 可拓创新方法及其应用研究进展[J]. 工业工程, 2012, 15(1): 131–137
YANG Chunyan, LI Xingsen. Research progress in extension innovation method and its applications[J]. Industrial engineering journal, 2012, 15(1): 131–137
[17] 杨春燕, 蔡文. 可拓学与矛盾问题智能化处理[J]. 科技导报, 2014, 32(36): 15–20
YANG Chunyan, CAI Wen. Extenics and intelligent processing of contradictory problems[J]. Science & technology review, 2014, 32(36): 15–20
[18] 杨春燕, 蔡文, 涂序彦. 可拓学的研究、应用与发展[J]. 系统科学与数学, 2016, 36(9): 1507–1512
YANG Chunyan, CAI Wen, TU Xuyan. Research, application and development on extenics[J]. Journal of systems science and mathematical sciences, 2016, 36(9): 1507–1512
[19] 蔡文, 杨春燕, 陈文伟, 等. 可拓集与可拓数据挖掘[M]. 北京: 科学出版社, 2008.
[20] 杨春燕, 蔡文. 可拓数据挖掘研究进展[J]. 数学的实践与认识, 2009, 39(4): 134–141
YANG Chunyan, CAI Wen. Recent progress in extension data mining[J]. Mathematics in practice and theory, 2009, 39(4): 134–141
[21] 陈晓华, 刘大莲, 田英杰, 等. 可拓支持向量分类机[J]. 智能系统学报, 2018, 13(1): 147–151
CHEN Xiaohua, LIU Dalian, TIAN Yingjie, et al. Extension support vector classification machine[J]. CAAI transactions on intelligent systems, 2018, 13(1): 147–151
[22] 赵燕伟, 朱芬, 桂方志, 等. 基于可拓距的改进k-means聚类算法[J]. 智能系统学报, 2020, 15(2): 344–351
ZHAO Yanwei, ZHU Fen, GUI Fangzhi, et al. Improved k-means algorithm based on extension distance[J]. CAAI transactions on intelligent systems, 2020, 15(2): 344–351
[23] 盛骤, 谢式千. 概率论与数理统计及其应用[M]. 2版. 北京: 高等教育出版社, 2010.
[24] YANG Sixiao, TIAN Yingjie, ZHANG Chunhua. Rule extraction from support vector machines and its applications[C]//2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. Lyon: IEEE, 2011: 221?224.
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