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JACIII Vol.19 No.1 pp. 152-157
doi: 10.20965/jaciii.2015.p0152
(2015)

Paper:

Supporting System for Quiz in Large Class – Automatic Keyword Extraction and Browsing Interface –

Haruhiko Takase*, Hiroharu Kawanaka*, and Shinji Tsuruoka**

*Graduate School of Engineering, Mie University, 1577 Kurima-Machiya, Tsu, Mie 514-8507, Japan

**Graduate School of Regional Innovation Studies, Mie University, 1577 Kurima-Machiya, Tsu, Mie 514-8507, Japan

Received:
October 15, 2013
Accepted:
July 25, 2014
Published:
January 20, 2015
Keywords:
e-learning, keyword extraction, text mining, natural language processing
Abstract
We focus on developing an e-learning system that supports the grasping of misunderstanding from descriptive answers. We propose real-time keyword extraction and an interface for grasping misunderstanding based on extracted keywords. The system extracts keywords without extra information. Teachers find majormisunderstandings by using the proposed interface, which consists of two views – keyword and description. Using these views, teachers browse answers in three steps – finding keywords, reading around keywords, and reading full answers. We use experiments to demonstrate the effectiveness of our system, this proposed keyword extraction extracts expected words. Subjects evaluate the proposed interface for its effectiveness in grasping misunderstandings. Using our proposed, teachers found major misunderstandings quickly and easily.
Cite this article as:
H. Takase, H. Kawanaka, and S. Tsuruoka, “Supporting System for Quiz in Large Class – Automatic Keyword Extraction and Browsing Interface –,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.1, pp. 152-157, 2015.
Data files:
References
  1. [1] S. A. Gaucii, A. M. Dantas, D. A. Williams, and R. E. Kemm, “Promoting student-centered active learning in lectures with a personal response system,” Advances in Physiology Education, Vol.33, pp. 60-71, 2007.
  2. [2] Moodle.org: open-source community-based tools for learning
    http://moodle.org/ [Accessed 2012]
  3. [3] Blackboard International,
    http://www.blackboard.com/ [Accessed 2012]
  4. [4] T. Ishioka and M. Kameda, “Automated Japanese Essay Scoring System based on Articles Written by Experts,” Proc. of the 21st Int. Conf. on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pp. 233-240, 2006.
  5. [5] J. Villalon and R. A. Calvo, “Concept Maps as Cognitive Visualizations of Writing Assignments,” Educational Technology & Society, Vol.14, No.3, pp. 16-27, 2011.
  6. [6] M. Miura, S. Kunifuji, and Y. Sakamoto, “AirTransNote: An Instant Note Sharing and Reproducing System to Support Students Learning,” Proc. of Seventh IEEE Int. Conf. on Advanced Learning Technologies (ICALT 2007), pp. 175-179, 2007.
  7. [7] T. Nakamura, T. Imai, H. Takase, N. Morita, H. Kawanaka, and S. Tsuruoka, “E-Learning System to Support Teacher’s Awareness for Misunderstanding in Quiz,” Proc. of the Int. Joint Conf. on Neural Networks 2010, pp. 1704-1708, 2010.
  8. [8] K. Spärck Jones, “A statistical interpretation of term specificity and its application in retrieval,” J. of Documentation, Vol.28, No.1, pp. 11-21, 1972.
  9. [9] T. Kudo, K. Yamamoto, and Y. Matsumoto, “Applying Conditional Random Fields to Japanese Morphological Analysis,” Proc. of the 2004 Conf. on Empirical Methods in Natural Language Processing, pp. 230-237, 2004.
  10. [10] H. Nakagawa and T. Mori, “Automatic Term Recognition based on Statistics of Compound Nouns and their Components,” Terminology, Vol.9, No.2, pp. 201-219, 2003.
  11. [11] H. Nakagawa, A. Maeda, and H. Kojima, “Automatic Domain Terminology Extraction System “Gensen Web”,” 2003.
    http://gensen.dl.itc.u-tokyo.ac.jp/gensenweb eng.html [Accessed 2012]
  12. [12] T. Kudo and H. Kazawa, “Web Japanese N-gram Version 1,” Gengo Shigen Kyokai, 2007.

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