Learning Effect of Implicit Learning in Joining-in-type Robot-assisted Language Learning System

Authors

  • AlBara Khalifa Graduate School of Science and Engineering, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan https://orcid.org/0000-0002-3823-7113
  • Tsuneo Kato Graduate School of Science and Engineering, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan
  • Seiichi Yamamoto Graduate School of Science and Engineering, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan

DOI:

https://doi.org/10.3991/ijet.v14i02.9212

Keywords:

Computer Assisted Language Learning (CALL), Robot Assisted Language Learning (RALL), Implicit Learning, Corrective Feedback

Abstract


The introduction of robots into language learning systems has been highly useful, especially in motivating learners to engage in the learning process and in letting human learners converse in more realistic conversational situations. This paper describes a novel robot-assisted language learning system that induces the human learner into a triad conversation with two robots through which he or she improves practical communication skills in various conversational situations. The system applies implicit learning as the main learning style for conveying linguistic knowledge, in an indirect way, through conversations on several topics. A series of experiments was conducted using 80 recruited participants to evaluate the effect of implicit learning and the retention effect in a joining-in-type robot-assisted language learning system. The experimental results show positive effects of implicit learning and repetitive learning in general. Based on these experimental results, we propose an improved method, integrating implicit learning and tutoring with corrective feedback in an adaptive way, to increase performance in practical communication skills even for a wide variety of proficiency of L2 learners.

Author Biographies

AlBara Khalifa, Graduate School of Science and Engineering, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan

AlBara Khalifa received his B.S. degree in Computer Engineering from King Fahd University of Petroleum and Minerals in 2002, M.S. degree in Software Engineering from Liverpool University in 2010, M.S. in Engineering from Doshisha University in 2015. He is a lecturer at the Computer Science and Engineering College in Taibah University, Medina, Saudi Arabia and is currently studying Ph.D. degree at the Graduate School of Science and Engineering, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan.

Tsuneo Kato, Graduate School of Science and Engineering, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan

Tsuneo Kato received his B.E., M.E., and Ph.D. degrees from the University of Tokyo in 1994, 1996, and 2011. He joined Doshisha University in 2015, where he is currently an associate professor in the Department of Intelligent Information Engineering. He had previously worked at KDDI R&D Laboratories Inc. since 1996. He has been engaged in the research and development of automatic speech recognition and intelligent user interfaces. He received an IPSJ Kiyasu Special Industrial Achievement Award in 2011. He is a member of IPSJ, ASJ, IEICE, and IEEE.

Seiichi Yamamoto, Graduate School of Science and Engineering, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan

Seiichi Yamamoto received B.S., M.S., and Ph.D. degrees from Osaka University in 1972, 1974, and 1983. He joined Kokusai Denshin Denwa Co. Ltd. in April 1974 and ATR Interpreting Telecommunications Research Laboratories in May 1997. He was appointed president of ATR-ITL in 1997. He is currently a Professor in the Department of Information Systems Design, Faculty of Science and Engineering, Doshisha University, Kyoto, Japan. His research interests include digital signal processing, speech recognition, speech synthesis, natural language processing, spoken language processing, spoken language translation, and multi-modal dialogue processing. He received Technology Development Awards from the Acoustical Society of Japan in 1995 and 1997, a best paper award from the Information and Systems Society of IEICE in 2006, and a telecom-system technology award from the Telecommunications Advancement Foundation in 2007. Dr. Yamamoto is a member of the ASJ, the IPSJ, the IEEE (Fellow), and IEICE Japan (Fellow).

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Published

2019-01-30

How to Cite

Khalifa, A., Kato, T., & Yamamoto, S. (2019). Learning Effect of Implicit Learning in Joining-in-type Robot-assisted Language Learning System. International Journal of Emerging Technologies in Learning (iJET), 14(02), pp. 105–123. https://doi.org/10.3991/ijet.v14i02.9212

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Papers