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JAMIOLAS 3.0: Supporting Japanese Mimicry and Onomatopoeia Learning Using Sensor Data

Author

Listed:
  • Bin Hou

    (University of Tokushima, Japan)

  • Hiroaki Ogata

    (University of Tokushima, Japan)

  • Masayuki Miyata

    (University of Tokushima, Japan)

  • Mengmeng Li

    (University of Tokushima, Japan)

  • Yuqin Liu

    (University of Tokushima, Japan)

Abstract

In this article, the authors propose an improved context-aware system to support the learning of Japanese mimicry and onomatopoeia (MIO) using sensor data. In the authors’ two previous studies, they proposed a context-aware language learning assistant system named JAMIOLAS (JApanese MImicry and Onomatopoeia Learning Assistant System). The authors used wearable sensors and sensor networks, respectively, to support learning Japanese MIO. To address the disadvantages of the previous systems, the authors propose a new learning model that can support learning MIO, using sensor data and the sensor network to enable context-aware learning by either initiating the creation of context or detecting context automatically.

Suggested Citation

  • Bin Hou & Hiroaki Ogata & Masayuki Miyata & Mengmeng Li & Yuqin Liu, 2010. "JAMIOLAS 3.0: Supporting Japanese Mimicry and Onomatopoeia Learning Using Sensor Data," International Journal of Mobile and Blended Learning (IJMBL), IGI Global, vol. 2(1), pages 40-54, January.
    • Handle: RePEc:igg:jmbl00:v:2:y:2010:i:1:p:40-54
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