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Generation Method of Multiple-Choice Cloze Exercises in Computer-Support for English-Grammar Learning

  • Ayse Saliha Sunar
  • Dai Inagi
  • Yuki Hayashi
  • Toyohide Watanabe
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 14)

Abstract

With many remarkable advances in technology, not only studying with tutor at school but also studying through computer at home become preferable. Intelligent Tutoring System (ITS) is one of the research fields which aim to support the individual learning intellectually. To provide the learning material of the domain knowledge in many ITS, the learning materials are statically associated with each other in advance and given to student based on her/his understanding state. Motivating student and making them more interested in the learning content is the system’s task in the computer-supported systems. If students study on content which they are interested in, learning activity becomes more effective. Our research objective is to construct a system which automatically generates multiple-choice cloze exercises from text input by the student. We focus on supporting individual study of learning English grammar. In this paper, we propose a representation method of English grammar by Part-Of-Speech (POS) tags and words, the calculation procedure for estimating the understanding state of student in the student model, and the learning strategy for generating the next exercise based on the student model.

Keywords

Domain Knowledge Learning Strategy Conditional Random Field Learning Content Input Text 
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.
    Corbett, A.T., Koedinger, K.R., Anderson, J.R.: Handbook of Human-Computer Interaction, pp. 849–874 (1997)Google Scholar
  2. 2.
    Heilman, M., Eskenazi, M.: Language Learning: Challenges for Intelligent Tutoring Systems. In: Proc. of the Workshop of Intelligent Tutoring Systems for Ill-Defined Domains, 8th International Conference on Intelligent Tutoring System, pp. 20–28 (2006)Google Scholar
  3. 3.
    Kyriakou, P., Hatzilygeroudis, I., Garofalakis, J.: A Tool for Managing Domain Knowledge and Helping Tutors in Intelligent Tutoring Systems. Journal of Universal Computer Science 16(19), 2841–2861 (2010)Google Scholar
  4. 4.
    Faulhaber, A., Melis, E.: An Efficient Student Model Based on Student Performance and Metadata. In: 18th European Conference on Artificial Intelligent (ECAI 2008). Frontiers in Artificial Intelligent and Applications (FAIA), vol. 178, pp. 276–280. IOS Press (2008)Google Scholar
  5. 5.
    Goto, T., Kojiri, T., Watanabe, T., Iwata, T., Yamada, T.: Automatic Generation System of Multiple-Choice Cloze Question and its Evaluation. KM and E-Learning: An International Journal 2(3), 210–224 (2010)Google Scholar
  6. 6.
    Tsuruoka, Y., Tsujii, J.: Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data. In: Proc. of HLT/EMNLP 2005, pp. 467–474 (2005)Google Scholar
  7. 7.
    Collins, M., Duffy, N.: New Ranking Algorithms for Parsing and tagging: Kernels over Discrete Structures, and the Voted Perceptron. In: Proc. of 40th Annual Meeting of the Association for Computational Linguistics, pp. 263–270 (2007)Google Scholar
  8. 8.
    Sang, T.K., Veenstra, J.: Representing Text Chunks. In: Proc. of EACL 1999, pp. 173–179 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ayse Saliha Sunar
    • 1
  • Dai Inagi
    • 2
  • Yuki Hayashi
    • 1
  • Toyohide Watanabe
    • 1
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan
  2. 2.Faculty of EngineeringNagoya UniversityNagoyaJapan

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