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)


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.


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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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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