web挖掘研究及其在远程教育中的应用
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摘要
随着计算机技术和Web技术的发展和广泛应用,远程教育发展到了基于Internet网络的第三代远程教育。当前,远程教育已经成网络研究和应用的热点之一。近年来,远程教育系统的应用在国内外取得了长足发展,很多大学和一些教育企业都建立了基于web的远程教育站点。
现有的Web远程教育站点基本上都是静态的,学习资源很少更新。参加远程教育的学生具有不同的个性和层次,目前大部分远程教育站点内容单一、知识深度一致,没有依据学生个人的实际情况量身订作、因材施教,缺少针对学生个性的服务。Web课件的设计和教学依据原有的传统面授教学规律,没有考虑远程学生个体的差异性和远程教育特有的网络环境。然而,现在大部分远程教育站点都积累了大量的有用信息,但是,这些信息没有被充分地利用。本文在对Web挖掘的概念和过程作了详细研究的基础上,将Web挖掘技术应用到远程教育中,建立基于Web挖掘的智能化个性化远程教育模型和学习环境,并给出了实现过程。
本文首先分析了现代远程教育的特点,对数据挖掘领域中的概念和算法进行了探讨,提出了基于Web挖掘的个性化远程教育模型。对Web挖掘的最新技术和发展方向作了全面的分析,详细讨论了Web挖掘的概念和处理过程;然后本文提出了应用多维数据立方体对Web日志进行多维关联规则挖掘的算法和在Web站点中自动发现那些存储位置同用户期望的位置不同的Web页面的算法,并将Web日志分析和挖掘中获得的规则和模式应用到宁波电大远程开放教育系统网站中,优化网站的效率。最后分析和展望了Web挖掘技术的发展和在远程教育中的应用前景。
With further development and application of computer technology and Web technology, Distance Education has also developed into a third generation. And Web Based Distance Education is currently one of the hottest areas on Web research and application. During these years, the application of Distance Educational System has made great achievements at home and abroad. At the same time, more and more universities and educational enterprises have now created their own Distance Education websites.
At present, many Web-based Distance Education systems are usually static and the study resources are also seldom updated. Moreover, these systems do not personalize the interaction or customize the learning materials to meet individual student’s needs, since the students are of different levels and have different learning habits. And the design and teaching of the course materials for these systems are directed by the traditional class experience too, without the consideration of the learning differences between individual learners and Distance Education environment. However, a lot of information about student learning has been accumulated in most Distance Education websites, but it is not efficiently used. Therefore, based on the careful research of Web Mining and its procedures, in this paper we try to apply the Web Mining technology for Distance Education and also manage to present an intelligent and customized Distance Education model with suitable learning environment as well as all the realization process.
The thesis is organized as following. First, the features of modern Distance Education and its characters are presented, and the concept and algorithm of Data Mining is also discussed so as to introduce the Web Mining based intelligent and customized Distance Education Model. Afer the analysis on the web mining techniques and new trends as well as the detailed discussion about the concept and process of Web Mining, the author proposes an algorithm to apply the Multi-Dimensional Data Cube for mining of Multi-Dimensional Association Rules, and an algorithm to automatically find pages in a website whose saving location is different from where visitors expect to find. Meanwhile, the paper uses the rules and patterns obtained from the Web Log analyzing and mining into the building of NBTVU Open Education Website to optimize it’s efficiency. Finally, the paper also analyzes the development of Web Mining Technology, and gives the perspective of the Intelligent Distance Education model.
现有的Web远程教育站点基本上都是静态的,学习资源很少更新。参加远程教育的学生具有不同的个性和层次,目前大部分远程教育站点内容单一、知识深度一致,没有依据学生个人的实际情况量身订作、因材施教,缺少针对学生个性的服务。Web课件的设计和教学依据原有的传统面授教学规律,没有考虑远程学生个体的差异性和远程教育特有的网络环境。然而,现在大部分远程教育站点都积累了大量的有用信息,但是,这些信息没有被充分地利用。本文在对Web挖掘的概念和过程作了详细研究的基础上,将Web挖掘技术应用到远程教育中,建立基于Web挖掘的智能化个性化远程教育模型和学习环境,并给出了实现过程。
本文首先分析了现代远程教育的特点,对数据挖掘领域中的概念和算法进行了探讨,提出了基于Web挖掘的个性化远程教育模型。对Web挖掘的最新技术和发展方向作了全面的分析,详细讨论了Web挖掘的概念和处理过程;然后本文提出了应用多维数据立方体对Web日志进行多维关联规则挖掘的算法和在Web站点中自动发现那些存储位置同用户期望的位置不同的Web页面的算法,并将Web日志分析和挖掘中获得的规则和模式应用到宁波电大远程开放教育系统网站中,优化网站的效率。最后分析和展望了Web挖掘技术的发展和在远程教育中的应用前景。
With further development and application of computer technology and Web technology, Distance Education has also developed into a third generation. And Web Based Distance Education is currently one of the hottest areas on Web research and application. During these years, the application of Distance Educational System has made great achievements at home and abroad. At the same time, more and more universities and educational enterprises have now created their own Distance Education websites.
At present, many Web-based Distance Education systems are usually static and the study resources are also seldom updated. Moreover, these systems do not personalize the interaction or customize the learning materials to meet individual student’s needs, since the students are of different levels and have different learning habits. And the design and teaching of the course materials for these systems are directed by the traditional class experience too, without the consideration of the learning differences between individual learners and Distance Education environment. However, a lot of information about student learning has been accumulated in most Distance Education websites, but it is not efficiently used. Therefore, based on the careful research of Web Mining and its procedures, in this paper we try to apply the Web Mining technology for Distance Education and also manage to present an intelligent and customized Distance Education model with suitable learning environment as well as all the realization process.
The thesis is organized as following. First, the features of modern Distance Education and its characters are presented, and the concept and algorithm of Data Mining is also discussed so as to introduce the Web Mining based intelligent and customized Distance Education Model. Afer the analysis on the web mining techniques and new trends as well as the detailed discussion about the concept and process of Web Mining, the author proposes an algorithm to apply the Multi-Dimensional Data Cube for mining of Multi-Dimensional Association Rules, and an algorithm to automatically find pages in a website whose saving location is different from where visitors expect to find. Meanwhile, the paper uses the rules and patterns obtained from the Web Log analyzing and mining into the building of NBTVU Open Education Website to optimize it’s efficiency. Finally, the paper also analyzes the development of Web Mining Technology, and gives the perspective of the Intelligent Distance Education model.
引文
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[2] Susbmita Mitra, Sankar K.Pal , Pabitra Mitra. Data Mining in Soft Computing Framework:A Survey.
[3] Jiawei Han, Micheline Kambr.数据挖掘概念与技术.北京:高等教育出版社, 2001.5
[4]韩家炜,孟小峰,王静,李盛恩. Web挖掘研究.计算机研究与发展, 2002.4
[5]张玉峰,徐敏刚.计算机辅助教学模式的演变及前景.武汉大学学报(社会科学版),2000(1)
[6]陈志锋,远程教学系统的智能化方案研究.苏州大学学报(自然科学版), 2001.1
[7]梁革英,个性化教育——远程教育的基本模式.广西民族学院学报(自然科学版), 2001(2)
[8]申瑞民,舒蓓,张同珍.个性化数字服务.微电子学与计算机, 2001(1)
[9]舒蓓,申瑞民,王加俊.个性化的远程学习模型.计算机工程与应用. 2001.9
[10]汪启军,申瑞民.基于Web Mining的智能化、个性化的远程教育模型研究.计算机工程2000.12.
[11]张维明主编.数据仓库原理与应用电子工业出版社, 2002.3
[12]朱明.数据挖掘.合肥:中国科学技术大学出版社,2002.5
[13] O. R. Zaiane, M. Xin, J. Han. Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. Proc. Advances in Digital Libraries Conf. (ADL'98) ,. 19-29, Santa Barbara, CA, April 1998.
[14] Fayyad, U.M, G.Piatetsky-Shapiro, P.Smyth, R.uthurusamy. Advances in Discovery and Data Mining, (AKDDM), AAAI/MIT Press, 1996
[15] Fayyad, U.M, G.Piatetsky-Shapiro, P.Smyth. From Data Mining to Knowledge Discovery: An Overview, in AKDDM, AAAI/MIT Press, 1996
[16] Rakesh Agrawal, Tomasz Imielinski, Arun Swami. Mining Association Rules between Sets of Items in Large Databases ,Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data.
[17] Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile, September 1994.
[18] R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems, 1999.1(1).
[19] W.H.Inmon著,王志海等译.数据仓库,北京:机械工业出版社, 2000.5
[20] J. Srivastava, R. Cooley, M. Deshpande, P-T. Tan. Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. To appear in SIGKDD Explorations, 2000,2(1)
[21]丁兴富.我国组织实施跨世纪的现代远程教育工程.现代远距离教育, 2001.1-3
[22] Y. Fu., Sandhu. K., and Shih. M., Fast Clustering of Web Users Based on Navigation Patterns, World Multiconference on Systemics, Cybernetics and Informatics (SCI/ISAS'99), Orlando, FL, Vol. 5, pages 560-567, Aug. 1999
[23] J.Han,J.Pie,and Y.Yin,Mining frequent patterns without candidate generation. In Proc2000 ACM-SIGMOD int.Conf.Management of Data(SIGMOD’00), Dalas,Tx,May 2000
[24] Edith Cohen,Mayur Datar,Shinji Fujiwara, Aristides Gionis,Piotr Indyk,Rajeev Motwani,Jeffey D.Ullman,Cheng Yang, Finding Interesting Associations without support Pruning
[25] J.Pei,J.Han,B.Mortazavi-asl,and H.Zhu,Mining access patterns efficiently from web logs, in Proc. Of the 4th Pacific-Asia Conf. On KDD,pages396-407,April 2000.
[26] Agrawal R.Mannila,H.Srikant R,Fast discovery of association rules,1996
[27] A.Savasere,E.Omiecinski,and S.Navathe,An efficient algorithm for mining association rules in large databases,Proceeding of the 21st International Conf. On Very large databases,1995
[28] H.Toivonen,Sampling large databases for association rules,Proceedings of the 22nd International Conf. On Very Large database,Bombay,India,September 1996
[29] H.Mannila,H.Toivonen,and A.Verkamo,efficient algorithm for discovering association rules. AAAI Workshop on KDD,1994,pp181-192
[30] J.S.Park,M.S.chen,and P.S.Yu,an effective hash-based algorithm for mining association rules, Proceeding ACM SIGMOD international Conf. On Management of data, P175-186, SanJose, CA, 1995.5
[31]杜芳,吴杰,张世永,基于Web的远程教育系统参考模型研究,计算机工程,2000(12)
[32]张蛾,冯秋红,宣慧玉,田增瑞,Web使用模式研究中的数据挖掘,2001(7)
[33]胡明晖,武化岩,胡明豪,数据挖掘技术及其在公共管理中的应用,中原工学院学报,2002(2)
[34]胡新华,骆雪超,Web开放远程教育系统中的CRM机制,微型电脑应用,2001(9)
[35]苏中,马少平,杨强,张宏江,基于Web-Log Mining的Web文档聚类,2002,13(1)
[36] B. Mobasher, N. Jain, E.H. Han, and J. Srivastava. Web mining: Pattern discovery from world wide web transactions. Technical Report TR-96050, 1996
[37] M. Perkowitz and O. Etzioni. Adaptive web sites: an ai challenge. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Nagoya, Japan, 1997.
[38] M. Perkowitz and O. Etzioni. Towards adaptive web sites: Conceptual framework and case study. Artificial Intelligence, To appear, 2000. 9
[39] Fu, Y., Shih, M., Creado, M., and Ju, C. Reorganizing Web Sites Based on User Access Patterns, International Journal of Intelligent Systems in Accounting, Finance and Management, 11(1), 2002
[40] Myra Spiliopoulou and Lukas C. Faulstich. WUM: A Web Utilization Miner. In International Workshop on the Web and Databases, Valencia, Spain, March 1998
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