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An Open Framework for Smart and Personalized Distance Learning

  • Ruimin Shen
  • Peng Han
  • Fan Yang
  • Qiang Yang
  • Joshua Zhexue Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2436)

Abstract

Web based learning enables more students to have access to the distance-learning environment and provides students and teachers with unprecedented flexibility and convenience. However, the early experience of using this new learning means in China exposes a few problems. Among others, teachers accustomed to traditional teaching methods often find it difficult to put their courses online and some students, especially the adult students, find themselves overloaded with too much information. In this paper, we present an open framework to solve these two problems. This framework allows students to interact with an automated question answering system to get their answers. It enables teachers to analyze students learning patterns and organize the webbased contents efficiently. The framework is intelligent due to the data mining and case-based reasoning features, and user-friendly because of its personalized services to both teachers and students.

Keywords

Association Rule Personalized Service Open Framework Knowledge Point Subsequence Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    C. Groeneboer, D. Stockley, T. Calvert: Virtual-U: A collaborative model for online learning environments, In: Proceedings of the Second International Conference on Computer Support for Collaborative Learning, Toronto, Ontario, December 1997.Google Scholar
  2. 2.
  3. 3.
    R. Agrawal and R. Srikant: Fast algorithms for mining association rules. In: Proceedings of VLDB’94, Santiago, Chile (1994), 487–499.Google Scholar
  4. 4.
    J. Han and Y. Fu: Discovery of multiple-level association rules from large databases. In: Proceedings of VLDB’95, Zürich, Switzerland (1995), 420–431.Google Scholar
  5. 5.
    R. Srikant and R. Agrawal: Mining generalized association rules. In: Proceedings of VLDB’95, Zürich, Switzerland (1995), 407–419.Google Scholar
  6. 6.
    R. Srikant and R. Agrawal: Mining quantitative association rules in large relational tables. In: Proceedings of SIGMOD’96, Montreal, Canada (1996), 1–12.Google Scholar
  7. 7.
    J. Breeze, D. Heckerman, and C. Kadie: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in AI, Madison, WI. (1998).Google Scholar
  8. 8.
    S. Chee, J. Han, and K. Wang. RecTree: An Efficient Collaborative Filtering Method. In: Proceedings of the DaWaK 2001, 141–151.Google Scholar
  9. 9.
    Q. Yang, H. Zhang, and I.T. Li: Mining Web Logs for Prediction Models in WWW Caching and Prefetching. In: Proceeding of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD’01, Industry Applications Track, San Francisco, California, USA (2001), 473–478.Google Scholar
  10. 10.
    J. Pitkow and P. Pirolli: Mining Longest Repeating Subsequences to Predict WWW Surfing. In: Proceedings of the USENIX Annual Technical Conference(1999).Google Scholar
  11. 11.
    V. Ganti, J. Gehrke, and R. Ramakrishnan: Mining very large databases. COMPUTER, Vol. 32, No. 8, (1999), 38–45.CrossRefGoogle Scholar
  12. 12.
    Z. Zhang and Q. Yang: Feature Weight Maintenance in Case Bases Using Introspective Learning. Journal of Intelligent Information Systems, Vol. 16, Kluwer Academic Publishers (2001), 95–116.zbMATHCrossRefGoogle Scholar
  13. 13.
    I.T. Li, Q. Yang, and K. Wang: Classification Pruning for Web-request Prediction. In: Poster Proceedings of the 10th World Wide Web Conference (WWW10), Hong Kong, China (2001).Google Scholar
  14. 14.
    Z. Su, Q. Yang, H.J. Zhang, X. Xu, Y. Hu, and S. Ma: Correlation-based Web-Document Clustering for Web Interface Design. International Journal of Knowledge and Information Systems. (2002) 4:141–167.Google Scholar
  15. 15.
    Q. Yang and J. Wu: Enhancing the Effectiveness of Interactive Case-Based Reasoning with Clustering and Decision Forests. Applied Intelligence Journal, Vol 14. No. 1., Kluwer Academic Publishers (2001), 49–64.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ruimin Shen
    • 1
  • Peng Han
    • 1
  • Fan Yang
    • 1
  • Qiang Yang
    • 2
  • Joshua Zhexue Huang
    • 3
  1. 1.Department of Computer Science & EngineeringShanghai Jiaotong UniversityShanghaiChina
  2. 2.Department of Computer ScienceHong Kong University of Science and TechnologyHong Kong, China
  3. 3.E-Business Technology InstituteUniversity of Hong KongHong Kong, China

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