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Visualization of Atmosphere Information for Distance Education System Based on Fuzzy Inference Using Customized Knowledge

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A distance education system is designed based on fuzzy inference, where visualized atmosphere information is shared by all learners in a virtual classroom. It provides high aspirations, low isolated feeling, low stress, and high affinity to learners, and offers learner’s psychological information, individual difference information, and hints of system improvement to the system manager. The effect of visualized atmosphere information in the learner’s psychological states is confirmed by T score of POMS test for 15 graduate students using CAI contents, and comparison experiment with traditional distance education. The proposal of atmosphere information presentation of virtual classroom provides a first step in establishing Education Academy beyond Space Time (EAST).
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Twórcy
autor
  • Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan
  • Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan
autor
  • Applied Informatics, Faculty of Science and Engineering, Hosei University, Koganei 184- 8584, Japan
autor
  • Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan
autor
  • Japan Society for the Promotion of Science, Beijing Office, 616, Library of Chinese Acad- emy of Sciences (CAS), 33 Beisihuan Xilu, Zhongguan- cun, Haidian District, Beijing 100190
Bibliografia
  • [1] J. Kim, A. Lee, and H. Ryu, “Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model,” International Journal of Industrial Ergonomic, vol. 43, 2013, 450–461.
  • [2] E. Sung, R. E. Mayer, “Affective impact of navigational and signaling aids to e-learning,” Computers in Human Behavior, vol. 28, no. 2, 2012, 473–483. DOI: 10.1016/j.chb.2011.10.019.
  • [3] D. Zhang, J. L. Zhao, L. Zhou, J. and F. Nunamaker Jr., “Can e-learning replace classroom learning?,” Communications of the ACM, vol. 45, no. 5, 2004, 75–79. DOI: 10.1145/986213.986216.
  • [4] Y. Yamazaki, Y. Hatakeyama, F. Dong, K. Nomoto, and K. Hirota, “Fuzzy Inference based Mentality Expression for Eye Robot in Affinity Pleasure-Arousal Space,” Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), vol. 12, no. 3, 2008, 304–313.
  • [5] K. Hirota, “Toward the Realization of Casual Communication between Humans and Robots,” Plenary Talk, ISME 2010, Kokura, 2010.
  • [6] Z. Liu, M. Wu, D. Li, L. Chen, F. Dong, Y. Yamazaki, and K. Hirota, “Concept of Fuzzy Atmosfield for Representing Communication Atmosphere and its Application to Humans-Robots Interaction,” Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), vol. 17, no.1, 2013, 7–13.
  • [7] B. Kermanshahi and K. Hirota, “High-Tech CAI Series: Fuzzy Logic, AI, and Neural Networks,” Computer Software Development Co., Ltd., 1997.
  • [8] V. Pollock, D. W. Cho, D. Reker, and J. Volavka, “Profile of Mood States: the Factors and Their Physiological Correlates,” The Journal of Nervous and Mental Disease, vol. 167, no. 10, 1979, 612–614.
  • [9] J. A. Garcia Sanchez, A. Shibata, F. Dong, and K. Hirota, “Deep Level Emotion Understanding based on Customized Knowledge for Agent to Agent Communication,” IWACII 2014 (University of Fukui), IWACII2014-02, 2014, 91–99.
  • [10] K. Ohnishi, F. Dong, and K. Hirota, “Atmosphere Understanding for Humans Robots Interaction Based on SVR and Fuzzy Set,” Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), vol. 18, no. 1, 2014, 62–70
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4e3ca325-97ff-4ffe-8dd4-fcab1e57fc06
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