数据挖掘技术在网络教育平台中的应用研究
|
摘要
数据挖掘是从大量的、不完全的、有噪声的、模糊的、随机的数据中,提取隐含在其中的、人们事先不知道的、但又潜在有用的信息和知识的过程。近十几年来,数据挖掘技术已经得到了广泛的研究,并在商业、金融、医疗等众多领域得到了成功地应用,但在教育领域中应用的还很少。随着信息技术的发展,信息技术在教学中的应用也越来越广泛,出现了各种各样的教育教学平台,在网络教育平台快速发展的同时,积累了很多数据,如用户的访问日志信息、注册信息、考试成绩信息、交流信息等,人们往往忽视了这些数据的重要性,造成了资源的极大浪费,这些缺点限制了网络教育平台的继续发展。
本文针对这一问题提出了将数据挖掘技术运用于网络教育平台的观点,指出了数据挖掘技术能够很好的解决网络教学中的许多问题,数据挖掘在网络教育平台的应用大有前景。
本文主要进行了以下几个方面的研究:
1.数据挖掘基本知识的深入研究与探讨,为后面各章节的运用奠定基础。
2.课程推荐模块中聚类规则的应用研究。首先分析了课程推荐在网络教育平台中的重要性,然后详细阐述了聚类规则挖掘在课程推荐模块的应用过程。
3.成绩分析模块中分类规则的应用研究。分析了现有成绩分析的不足,指出考试系统中学习者基本信息与考试成绩间是存在某种联系的,通过决策树分类规则挖掘技术在成绩分析中的应用,实现对学习者成绩的预测。
4.数据挖掘技术在辅助教师决策进行学习者信息分析方面的应用研究。
Data mining technology is a procedure of distilling available information and knowledge from mass , incompleted and random data . Data mining has been studied and applied recently, and it has been applied in many domains successfully, such as business, finance and medical treatment. With the development of information technology, information technology has been widely applied in education and many education platforms appeared to promote the development of lifelong learning and civil learning .But with the fast development of those platforms, they have accumulated great quantity of data, and the data often are ignored. On the one hand, we are at a loss to handle those complex education platforms, and on the other hand, many useful data are ignored and wasted . this paper is to solve this problem . This paper does a research on how to apply data mining to education platforms, and point out data mining can solve many problems in our web -based learning . It is promising for data mining to be used in web-based learning platforms.
The main work of this paper is followed:
1. The research and discussing of the basic knowledge of data mining.
2. The research of applying cluster-based Collaborative Filtering recommendation technology to course recommendation module. The importance of course recommendation in web-base learning is then point out, and then the process of applying cluster-based Collaborative Filtering recommendation technology to course recommendation module is analyzed in detail.
3. The research of applying data mining to score analysis module. The relationship between the student' s basic information and his/her score is analyzed. It becomes possible to predict students' score with the decision tree classification rule mining.
4. The research of applying data mining to help teacher make decision and get familiar with the students' activities .
本文针对这一问题提出了将数据挖掘技术运用于网络教育平台的观点,指出了数据挖掘技术能够很好的解决网络教学中的许多问题,数据挖掘在网络教育平台的应用大有前景。
本文主要进行了以下几个方面的研究:
1.数据挖掘基本知识的深入研究与探讨,为后面各章节的运用奠定基础。
2.课程推荐模块中聚类规则的应用研究。首先分析了课程推荐在网络教育平台中的重要性,然后详细阐述了聚类规则挖掘在课程推荐模块的应用过程。
3.成绩分析模块中分类规则的应用研究。分析了现有成绩分析的不足,指出考试系统中学习者基本信息与考试成绩间是存在某种联系的,通过决策树分类规则挖掘技术在成绩分析中的应用,实现对学习者成绩的预测。
4.数据挖掘技术在辅助教师决策进行学习者信息分析方面的应用研究。
Data mining technology is a procedure of distilling available information and knowledge from mass , incompleted and random data . Data mining has been studied and applied recently, and it has been applied in many domains successfully, such as business, finance and medical treatment. With the development of information technology, information technology has been widely applied in education and many education platforms appeared to promote the development of lifelong learning and civil learning .But with the fast development of those platforms, they have accumulated great quantity of data, and the data often are ignored. On the one hand, we are at a loss to handle those complex education platforms, and on the other hand, many useful data are ignored and wasted . this paper is to solve this problem . This paper does a research on how to apply data mining to education platforms, and point out data mining can solve many problems in our web -based learning . It is promising for data mining to be used in web-based learning platforms.
The main work of this paper is followed:
1. The research and discussing of the basic knowledge of data mining.
2. The research of applying cluster-based Collaborative Filtering recommendation technology to course recommendation module. The importance of course recommendation in web-base learning is then point out, and then the process of applying cluster-based Collaborative Filtering recommendation technology to course recommendation module is analyzed in detail.
3. The research of applying data mining to score analysis module. The relationship between the student' s basic information and his/her score is analyzed. It becomes possible to predict students' score with the decision tree classification rule mining.
4. The research of applying data mining to help teacher make decision and get familiar with the students' activities .
引文
[1]JiaweiHan,M.Kamb.DataMining:Concepts and Techniques.2000
[2]G.Thamaraiselvi,A.Kaliammal.data mining:concepts and techniques,SRELS Journal of Information Management,Vol.41,No.4,December 2004,Paper AJ.p339-348
[3]Crist6bal.Romero,S.Ventura.Educational data mining:a survey from 1995 to 2005..Expert Systems with Applications 33:1(2007)135-146.
[4]F.Castro,A.Vellido,A.Nebot,F.Mugica.Applying Data Mining Techniques to e-Learning Problems.In:L.Jain,R.Tedman,D.Tedman(Eds.)Evolution of Teaching and Learning Paradigms in Intelligent Environment,Springer,2007,183-221
[5]李晓葵.远程教育:构建终身教育体系的有效途径.湘潭师范学报(社会科学版).2007年7月,Vol.29,No.4.pages164-166.
[6]邵峰晶,于忠清编著.数据挖掘原理与算法.中国水利水电出版社.2003
[7]黄绍君,杨炳儒等.知识发现及其应用研究回顾.计算机应用研究,2001.Vol.18,No.4,8-1-5,8页
[8]韩雪涛.基于网络的家校合作系统中个性化推荐的设计与实现.首都师范大学硕士论文.2006.
[9]李东海.数据挖掘技术在远程教学系统中的应用研究.首都师范大学硕士学位论文.2003
[10]C.Romero,S.Ventura,E.Garcia Salcines.Data mining in course management systems:Moodle case study and tutorial.Computer & Education 49(2007)1-17
[11]章成志.数据挖掘研究现状及最新进展.南京工业职业技术学院学报.2003,第2期.3-5
[12]康晓东主编.基于数据仓库的数据挖掘技术.机械工业出版社.2004.
[13]赵艳厂.数据挖掘中聚类算法研究与仿真.北京邮电大学博士论文.2003.
[14]沙志强.数据挖掘技术在智能推荐系统中的研究与应用.北京工业大学硕士论文.2005.
[15]Feng-Hsu Wang,Hsiu-Mei Shao,Effective personalized recommendation based on time-framed navigation clustering and association mining.Expert System with Application 27(2004)365-377.
[16]F.Wang,H.Shao.Effective personalized recommendation based on time-framed navigation clustering and association mining.Expert Systems with Applications 27(2004)365-377
[17]T.Tang,G.McCalla.Smart Recommendation for an Evolving e-Leaming System:Architecture and Experiment.International Journal on e-Learning 4:1(2005)105-129
[18]程岩,肖小云,吴洁倩.基于聚类分析的电子商务推荐系统.计算机工程与应用.2005.24.pages175-177.
[19]王辉,高利军,王听忠.个性化服务中基于用户聚类的协同过滤推荐.计算机应用.2007.Vol.27,No.5.
[20]刘赫.一个基于聚类算法的推荐系统的设计与实现.吉林大学硕士论文.2005
[21]邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法.软件学报.2002.Vol.13,No.4 1000-9825/2002/13(04)0000-04.
[22]刘林东.Web挖掘在网上考试系统中的应用.中山大学硕士论文.2005
[23]魏涛.基于决策树的教育信息挖掘模型-DT-EIDM的设计与实现.上海海事大学硕士论文.2005.
[24]曲春锦.Aprioir_TID算法设计及其在教育决策信息挖掘中的应用.上海海事大学.硕士论文.2005
[25]赵红艳.决策树技术在学习者成绩分析中的应用研究.山东师范大学硕士论文.2007
[26]蔡勇,韩永国,刘自伟.数据挖掘技术在生源分析中的应用研究.计算机应用研究.2004年12期.
[27]范杰.基于数据挖掘的在线学习行为评估系统的设计与实现.国防科技大学同等学历申请硕士学位论文.2005
[28]L.Dringus,T.Ellis.Using data mining as a strategy for assessing asynchronous discussion forums.Computer & Education 45(2005)141-160
[29]D.Monk.Using Data Mining for e-Learning Decision Making.The Electronic Journal of e-Learning 3:1(2005)41-54
[2]G.Thamaraiselvi,A.Kaliammal.data mining:concepts and techniques,SRELS Journal of Information Management,Vol.41,No.4,December 2004,Paper AJ.p339-348
[3]Crist6bal.Romero,S.Ventura.Educational data mining:a survey from 1995 to 2005..Expert Systems with Applications 33:1(2007)135-146.
[4]F.Castro,A.Vellido,A.Nebot,F.Mugica.Applying Data Mining Techniques to e-Learning Problems.In:L.Jain,R.Tedman,D.Tedman(Eds.)Evolution of Teaching and Learning Paradigms in Intelligent Environment,Springer,2007,183-221
[5]李晓葵.远程教育:构建终身教育体系的有效途径.湘潭师范学报(社会科学版).2007年7月,Vol.29,No.4.pages164-166.
[6]邵峰晶,于忠清编著.数据挖掘原理与算法.中国水利水电出版社.2003
[7]黄绍君,杨炳儒等.知识发现及其应用研究回顾.计算机应用研究,2001.Vol.18,No.4,8-1-5,8页
[8]韩雪涛.基于网络的家校合作系统中个性化推荐的设计与实现.首都师范大学硕士论文.2006.
[9]李东海.数据挖掘技术在远程教学系统中的应用研究.首都师范大学硕士学位论文.2003
[10]C.Romero,S.Ventura,E.Garcia Salcines.Data mining in course management systems:Moodle case study and tutorial.Computer & Education 49(2007)1-17
[11]章成志.数据挖掘研究现状及最新进展.南京工业职业技术学院学报.2003,第2期.3-5
[12]康晓东主编.基于数据仓库的数据挖掘技术.机械工业出版社.2004.
[13]赵艳厂.数据挖掘中聚类算法研究与仿真.北京邮电大学博士论文.2003.
[14]沙志强.数据挖掘技术在智能推荐系统中的研究与应用.北京工业大学硕士论文.2005.
[15]Feng-Hsu Wang,Hsiu-Mei Shao,Effective personalized recommendation based on time-framed navigation clustering and association mining.Expert System with Application 27(2004)365-377.
[16]F.Wang,H.Shao.Effective personalized recommendation based on time-framed navigation clustering and association mining.Expert Systems with Applications 27(2004)365-377
[17]T.Tang,G.McCalla.Smart Recommendation for an Evolving e-Leaming System:Architecture and Experiment.International Journal on e-Learning 4:1(2005)105-129
[18]程岩,肖小云,吴洁倩.基于聚类分析的电子商务推荐系统.计算机工程与应用.2005.24.pages175-177.
[19]王辉,高利军,王听忠.个性化服务中基于用户聚类的协同过滤推荐.计算机应用.2007.Vol.27,No.5.
[20]刘赫.一个基于聚类算法的推荐系统的设计与实现.吉林大学硕士论文.2005
[21]邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法.软件学报.2002.Vol.13,No.4 1000-9825/2002/13(04)0000-04.
[22]刘林东.Web挖掘在网上考试系统中的应用.中山大学硕士论文.2005
[23]魏涛.基于决策树的教育信息挖掘模型-DT-EIDM的设计与实现.上海海事大学硕士论文.2005.
[24]曲春锦.Aprioir_TID算法设计及其在教育决策信息挖掘中的应用.上海海事大学.硕士论文.2005
[25]赵红艳.决策树技术在学习者成绩分析中的应用研究.山东师范大学硕士论文.2007
[26]蔡勇,韩永国,刘自伟.数据挖掘技术在生源分析中的应用研究.计算机应用研究.2004年12期.
[27]范杰.基于数据挖掘的在线学习行为评估系统的设计与实现.国防科技大学同等学历申请硕士学位论文.2005
[28]L.Dringus,T.Ellis.Using data mining as a strategy for assessing asynchronous discussion forums.Computer & Education 45(2005)141-160
[29]D.Monk.Using Data Mining for e-Learning Decision Making.The Electronic Journal of e-Learning 3:1(2005)41-54