Learning system for Japanese onomatopoeia’s nuance through creation task

Shuo Yang, Takashi Hashimoto, Guanhong Li, Xiao Yan Li

Research output: Contribution to journalArticle

Abstract

Japanese onomatopoeia is an important element to express feelings and experiences lively. It is very difficult for Japanese learners to acquire onomatopoeia, especially, its nuance. In this paper, based on traditional L2 learning theories, we propose a new learning method to improve the efficiency of learning Japanese onomatopoeias’ nuance - both explicit and implicit - for non-native speakers. The method for learning implicit nuance of onomatopoeia consists of three elements. First is studying the formal rules representing the explicit nuances of onomatopoeic words. Second is creating new onomatopoeic words by learners to utilize those formal rules. The last is giving feedback of relevance of the onomatopoeias created. We then show a learning system implementing the proposed method. In addition, to verify the effectiveness of the proposed method and the learning system, we conducted an experiment involving two groups of subjects. While the experiment group covers all the three elements of the proposed method, the control group involves no creation process, which is supposed to be a core element of our proposed method, instead, does an assessment process in which the participants assess the appropriateness of onomatopoeic words presented. Both groups were required to take two tests, before and after going through the learning process. The learning effect is defined as the difference between the scores gained from pre-learning test and post-learning test. The result confirms that the proposed method has significant effect in learning onomatopoeia for non-native speakers. Moreover, the comparison against the control group shows that the creation process is the key to bring the learning effect.

Original languageEnglish
Pages (from-to)331-339
Number of pages9
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume30
Issue number1
DOIs
Publication statusPublished - Jan 6 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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