1. 华东理工大学商学院 上海 200237;
2. 上海交通大学安泰经济与管理学院 上海 200052;
3. 中国航空无线电电子研究所 上海 200233;
4. 上海财经大学信息管理与工程学院 上海 200433;
5. 美国南加州大学信息科学研究所 Marina del Rey CA 90292
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
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.
李嘉, 张朋柱, 李欣苗, Jihie Kim. 一种通过挖掘研讨记录来促进学生思考的在线督导系统[J]. 现代图书情报技术, 2012, 28(4): 10-16.
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.
[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.
