Please wait a minute...
New Technology of Library and Information Service  2012, Vol. 28 Issue (4): 10-16    DOI: 10.11925/infotech.1003-3513.2012.04.02
Current Issue | Archive | Adv Search |
An Online Tutoring System Mining Discussion Logs to Stimulate Students Thinking
Li Jia1,2,3, Zhang Pengzhu2, Li Xinmiao4, Jihie Kim5
1. School of Business, East China University of Science and Technology, Shanghai 200237, China;
2. Antai College of Economics & Management, Shanghai Jiaotong University, Shanghai 200052, China;
3. Chinese Aeronautical Radio Electronics Research Institute, Shanghai 200233, China;
4. School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China;
5. Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, USA
Export: BibTeX | EndNote (RIS)      
Abstract  In order to stimulate student thinking and reduce service response time, the authors propose an online recommendation method that mines relevant information from discussion logs. The proposed method consists of six steps, namely technique term hierarchical tree building, task term extraction, document segmentation, feature extraction, topic identification, and document score calculation. Two experiments are designed to evaluate the proposed system. In the first experiment, three different document recommendation algorithms (i.e., TF-IDF, TF-IDF + topic filtering, TF-IDF + LSA + topic filtering) are compared, and the TF-IDF + topic filtering algorithm gets the best performance. In the second experiment, the proposed system is applied to one semester of online discussion, and the proposed system leads to better discussion participation as well as a higher level of perceived usefulness and ease of use. The results indicate that moderated relevant discussion record can be mined automatically, and providing such information can stimulate student's thinking and discussion.
Key wordsDiscussion log      Recommendation systems      Thinking stimulation      Online tutoring systems      E-learning     
Received: 29 February 2012      Published: 20 May 2012



Cite this article:

Li Jia, Zhang Pengzhu, Li Xinmiao, Jihie Kim. An Online Tutoring System Mining Discussion Logs to Stimulate Students Thinking. New Technology of Library and Information Service, 2012, 28(4): 10-16.

URL:     OR

[1] Caballé S, Xhafa F, Abraham A. Towards an Automatic Real-Time Assessment of Online Discussions in Computer-Supported Collaborative Learning Practices [C]. In: Proceedings of the 3rd International Conference on Digital Information Management(ICDIM 2008),London,UK.2008.

[2] Kim J, Shaw E, Feng D H, et al. Modeling and Assessing Student Activities in On-line Discussions [C]. In: Proceedings of the AAAI Workshop on Educational Data Mining.2006.

[3] LaVoie N, Psotka J, Lochbaum K E, et al. Automated Tools for Distance Learning [C]. In: Proceedings of the New Learning Technologies Conference,Orlando, FL,USA.2004.

[4] Lochbaum K, Psotka J, Streeter L. Exploiting Technology to Harness the Power of Peers [C]. In: Proceedings of the Interservice/Industry Training, Simulation and Education Conference,Orlando, FL,USA.2002.

[5] Feng D H, Kim J, Shaw E,et al. Towards Modeling Threaded Discussions Using Induced Ontology Knowledge [C]. In: Proceedings of the National Conference on Artificial Intelligence (AAAI-2006). AAAI Press, 2006.

[6] Kim J, Chern G, Feng D H, et al. Mining and Assessing Discussions on the Web Through Speech Act Analysis[C]. In: Proceedings of the ISWC'06 Workshop on Web Content Mining with Human Language Technologies.2006.

[7] Kim J, Shaw E, Ravi S, et al. Scaffolding On-Line Discussions with Past Discussions: An Analysis and Pilot Study of PedaBot [C].In: Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS 2008),Montreal, Canada.Springer,2008.

[8] Hearst M A. TextTiling: Segmenting Text into Multi-paragraph Subtopic Passages [J]. Computational Linguistics, 1997, 23(1): 33-64.

[9] Allan J. Incremental Relevance Feedback for Information Filtering [C]. In: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.1996: 270-278.

[10] Callan J. Learning While Filtering Documents Learning While Filtering Documents [C]. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York, NY, USA:ACM,1998.

[11] Fiscus J, Wheatley B. Overview of the TDT 2004 Evaluation and Results [C]. In: Proceedings of TDT-2004, 2004.

[12] Jones K S. A Statistical Interpretation of Term Specificity and Its Application in Retrieval [J]. Journal of Documentation, 1972, 28(1): 11-21.

[13] Salton G, Wong A, Yang C S. A Vector Space Model for Automatic Indexing [J]. Communications of the ACM, 1975, 18(11): 613-620.

[14] Salton G, Fox E A, Wu H. Extended Boolean Information Retrieval [J]. Communications of the ACM, 1983, 26(11): 1022-1036.

[15] Deerwester S, Dumais S T, Furnas G W, et al. Indexing by Latent Semantic Analysis [J]. Journal of the American Society for Information Science, 1990, 41(6): 391-407.

[16] Berry M W, Dumais S T, O'Brien G W,et al. Using Linear Algebra for Intelligent Information Retrieval [J]. SIAM Review, 1995,37(4): 573-595.

[17] Chowdhury G G. Introduction to Modern Information Retrieval [M]. McGraw-Hill Computer Science Series, 2004.

[18] Davis F D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology [J]. MIS Quarterly, 1989, 13(3): 319-340.

[19] Doll W J, Hendrickson A, Deng X D. Using Davis's Perceived Usefulness and Ease-of-Use Instruments for Decision Making: A Confirmatory and Multigroup Invariance Analysis [J]. Decision Sciences, 1998, 29(4): 839-869.

[20] Karahanna E, Straub D W. The Psychological Origins of Perceived Usefulness and Ease-of-Use [J]. Information & Management, 1999, 35(4): 237-250.
[1] Ma Li. Collaborative Filtering Recommendation Method Based on User Learning Tree[J]. 现代图书情报技术, 2016, 32(4): 72-80.
[2] Wang Hao, Ye Peng, Deng Sanhong. The Application of Machine-Learning in the Research on Automatic Categorization of Chinese Periodical Articles[J]. 现代图书情报技术, 2014, 30(3): 80-87.
[3] Li Cong. ECRec:e-Commerce Personalized Recommendation Management Based on Collaborative Filtering[J]. 现代图书情报技术, 2009, (10): 34-39.
[4] Xue Xiaoming,Niu Zhendong,Liu Rongjian. An Agent Based Analysis of Study Information in E-learning[J]. 现代图书情报技术, 2005, 21(5): 33-36.
[5] Shao Jing,Dang Haifeng,Bai Huixian,Zheng Qinghua. The Study on Solution to Sharing and Integration of  OPAC and E-learning Resources[J]. 现代图书情报技术, 2005, 21(5): 64-67.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938