Toward the Cognitive Analysis of Non-Native Speakers’ Handwriting in Japanese with iPad and LMS


Tablet computers have gathered attention in classrooms in Japanese university. Apple’s iPad is the most popular among several varieties. BeeDance is a learning management system (LMS) for iPads created by a Japanese company to facilitate active participations of learners. In this study, cognitive analyses of non-native speakers’ Japanese handwriting are examined through the uses of BeeDance in classrooms. First of all, the technological features of BeeDance system are introduced and the theoretical backgrounds from the perspective of technologically-enhanced learning are discussed. Secondly, the innovative classroom materials, activities, quizzes, and questionnaires created for BeeDance are presented using its five basic functions; response, image board, text board, recording, and file sharing. Through the system, teachers can send files and exercise questions from teacher’s iPad to learners’ iPads. Teachers can also monitor learners’ iPad screens in real time and show them to class through a projector at the same time. Then, the studies are conducted to analyze non-native speakers’ handwriting through the image board function of BeeDance. The orders of handwriting strokes can be monitored and recorded in order to analyze learners’ cognitive understanding of Japanese characters such as hiragana, katakana, and kanji. Even though we need further studies and experimental lessons to be more aware of cognitive understanding of Japanese learners, this is a new way of monitoring students’ learning behaviors in classroom. By maintaining the attention of the learners and gaining their confidence and satisfaction, BeeDance helps learners to maintain a desire to learn and succeed.

Author Information
Makoto Shishido, Tokyo Denki University, Japan
Rie Kudo, Tokyo Denki University, Japan
Nari Matsushima, Tokyo Denki University, Japan

Paper Information
Conference: ACTC2017
Stream: Integrating e-learning in classroom based language teaching

This paper is part of the ACTC2017 Conference Proceedings (View)
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Posted by James Alexander Gordon