Generating Personalized Answers by Constructing a Question Situation

  • Yanwen Wu
  • Zhenghong Wu
  • Yan Li
  • Jinling Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


This paper proposes a methodology to generate the personalized answer based on a question situation. To construct the question situation, personal learning characteristics should be obtained by principal component analysis and question type vector is used in the process of semantic analysis. After these analyses, the question situation is constructed based on a harmony network. The answer parameters, including answer depth and answer presentation pattern, are calculated by harmony function. According to these parameters, the personalized answer is matched by the adaptive neuro-fuzzy inference (ANFI). The system architecture of personalized answer generation is proposed in this paper and takes a learner’s question to demonstrate.


Learning Characteristic Learner Sample Expression Character Answer Parameter Knowledge Point 
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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  1. 1.
    Shuang, L., Chen, L.: The Comparison of Domestic and International Online Intelligent Question Answering System. China Educational Technology 1, 80–83 (2003)Google Scholar
  2. 2.
    Masui, F., Miyaguchi, M.: MAIMAI: A Question Answering System at NTCIR3 QAC-1. National Institute of Informatics (2003)Google Scholar
  3. 3.
    Rita, K., Wei-Peng, L., Maiga, C., Jia-Sheng, H.: Analyzing Problem’s Difficulty based on Neural Networks and Knowledge Map. Educational Technology & Society 7(2), 42–50 (2004)Google Scholar
  4. 4.
    Jonassen, D.J., Grabowski, B.L.: Handbook of Individual Differences, Learning and Instruction. Lawrence Erlbaum Associates. Inc., Mahwah (1993)Google Scholar
  5. 5.
    Rothwell, W.J., Kazanas, H.S.: Mastering the Instructional Design: a Systematic Approach Process, 3rd edn. John Wiley Sons Inc./Pfeiffer, San Francisco (2004)Google Scholar
  6. 6.
    Kramarski, B., Feldman, Y.: Internet in the Classroom: Effects on Reading, Comprehension, Motivation and Metacognitive Awareness. Education Media International 37(3), 149–155 (2000)CrossRefGoogle Scholar
  7. 7.
    Ruizhen, S.: Educational Psychology, 1st edn. Educational Press of Shanghai, Shanghai (1997)Google Scholar
  8. 8.
    Kahney, H.: Problem Solving: Current Issue, 2nd edn. Open University Press, Milton Keynes (1993)Google Scholar
  9. 9.
    Gagné, E.D., Yekovich, C.W., Yekovich, F.R.: The Cognitive Psychology of Scholl Learning. Harper Collins College Publishers, New York (1993)Google Scholar
  10. 10.
    Weihua, L.: The Design of Intelligent Answering Expert System in Web Teaching System. Journal of Beijing Union University (Natural Sciences) 18(3), 42–45 (2004)Google Scholar
  11. 11.
    Yajun, L., Yi, X., Lisha, G. (Nat. Sci. Ed.): Research and Implementation of Quick Location Algorithm for Intelligent Question Answering System. Journal of Southeast University 30(4), 410–413 (2003)Google Scholar
  12. 12.
    Peng, H., Ruimin, S., Fan, Y.: Application of Case Based Reasoning on Q&A System. Journal of Shanghai Jiao Tong University 37(3), 393–396 (2003)Google Scholar
  13. 13.
    Sadler-Smith, E., Allinson, C.W., Hayes, J.: Learning Preferences and Cognitive Style: Some Implications for Continuing Professional Development. Management Learning 31(2), 239–256 (2000)CrossRefGoogle Scholar
  14. 14.
    Qun, S., Ruimin, S., Wu, W.: Generalization and Association Pattern Mining Intelligent Answering Model Based on Knowledge Tree. Computer Engineering 29(17), 124–125 (2003)Google Scholar
  15. 15.
    Joo, Y.-J., Bong, M., Choi, H.-J.: Self-efficacy for Self-regulated Learning, Academic Aelf-efficacy, and Internet Self-efficacy in Web-based Instruction. Educational Technology Research and Development 48(2), 5–17 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yanwen Wu
    • 1
  • Zhenghong Wu
    • 1
  • Yan Li
    • 1
  • Jinling Li
    • 1
  1. 1.Department of Information & TechnologyCentral China Normal UniversityWuhanChina

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