講演抄録/キーワード |
講演名 |
2023-01-20 15:15
Multi-task Training with Joining-in-type Robot-assisted Language Learning System ○Yu Zha・Tsuneo Kato・Seiichi Yamamoto・Akihiro Tamura(Doshisha Univ.) ET2022-60 |
抄録 |
(和) |
(まだ登録されていません) |
(英) |
Introducing robots into language learning systems is effective, especially in motivating learners to engage in learning and allowing the learners to talk in a more realistic conversational environment. The joining-in-type robot-assisted language learning (JIT-RALL) system uses two robots (one as a teacher and one as a co-learner) to simulate a multi-party conversation, which can increase learning effects. However, previous JIT-RALL had single training tasks and a fixed process. In order to further increase learning effects, we introduced the well-ordered system approach from second language acquisition theory and proposed a multi-task training with JIT-RALL system. In this paper, we compared the learning effects of learners who participated in the previous single-task JIT-RALL system with those who participated in our proposed multi-task training with JIT-RALL system. We found that the learning effects of the learners who participated in our multi-task RALL system increased by 0.20 points. |
キーワード |
(和) |
/ / / / / / / |
(英) |
Joining-in-type robot-assisted language learning system / Multi-task training / Well-ordered system approach / Learning effects / / / / |
文献情報 |
信学技報, vol. 122, no. 348, ET2022-60, pp. 23-28, 2023年1月. |
資料番号 |
ET2022-60 |
発行日 |
2023-01-13 (ET) |
ISSN |
Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
PDFダウンロード |
ET2022-60 |