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On the effectiveness of Robot-Assisted Language Learning

Published online by Cambridge University Press:  05 January 2011

Sungjin Lee ,
Hyungjong Noh ,
Jonghoon Lee ,
Kyusong Lee ,
Gary Geunbae Lee ,
Seongdae Sagong  and
Munsang Kim
Sungjin Lee*
Affiliation:
Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), South Korea (email: junion@postech.ac.kr)
Hyungjong Noh*
Affiliation:
Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), South Korea (email: nohhj@postech.ac.kr)
Jonghoon Lee*
Affiliation:
Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), South Korea (email: jh21983@postech.ac.kr)
Kyusong Lee*
Affiliation:
Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), South Korea (email: kyusonglee@postech.ac.kr)
Gary Geunbae Lee*
Affiliation:
Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), South Korea (email: gblee@postech.ac.kr)
Seongdae Sagong*
Affiliation:
Center for Intelligent Robotics, Korea Institute of Science and Technology, South Korea (email: sdsagong@kist.re.kr)
Munsang Kim*
Affiliation:
Center for Intelligent Robotics, Korea Institute of Science and Technology, South Korea (email: munsang@kist.re.kr)
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Abstract

This study introduces the educational assistant robots that we developed for foreign language learning and explores the effectiveness of robot-assisted language learning (RALL) which is in its early stages. To achieve this purpose, a course was designed in which students have meaningful interactions with intelligent robots in an immersive environment. A total of 24 elementary students, ranging in age from ten to twelve, were enrolled in English lessons. A pre-test/post-test design was used to investigate the cognitive effects of the RALL approach on the students’ oral skills. No significant difference in the listening skill was found, but the speaking skills improved with a large effect size at the significance level of 0.01. Descriptive statistics and the pre-test/post-test design were used to investigate the affective effects of RALL approach. The result showed that RALL promoted and improved students’ satisfaction, interest, confidence, and motivation at the significance level of 0.01.

Keywords

Robot-assisted language learningintelligent computer-assisted language learningintelligent tutoring systemsinteractive learning environmentshuman robot interactions
Type
Research Article
Information
ReCALL , Volume 23 , Issue 1 , 05 January 2011 , pp. 25 - 58
Copyright
Copyright © European Association for Computer Assisted Language Learning 2011

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References

Ahn, D. H.Chung, M. (2004) One-Pass Semi-Dynamic Network Decoding Using a Subnetwork Caching Model for Large Vocabulary Continuous Speech Recongnition. IEICE TRANSACTIONS on Information and Systems, 87(5): 1164–1174.Google Scholar
Bobrow, D. G., Kaplan, R. M., Kay, M., Norman, D. A., Thompson, H.Winograd, T. (1977) GUS, a frame-driven dialog system. Artificial intelligence, 8(2): 155–173.CrossRefGoogle Scholar
Bohus, D.Rudnicky, A. I. (2005) Constructing accurate beliefs in spoken dialog systems. In: 2005 IEEE Workshop on Automatic Speech Recognition and Understanding. San Juan, Puerto Rico, 272–277.CrossRefGoogle Scholar
Bohus, D.Rudnicky, A. I. (2006) A K hypotheses + other belief updating model. In: AAAI Workshop on Stochastic Methods in Spoken Dialog Systems. Boston: MA.Google Scholar
Bohus, D.Rudnicky, A. I. (2009) The RavenClaw dialog management framework: Architecture and systems. Computer Speech and Language, 23(3): 332–361.Google Scholar
Brown, G. (1986) Investigating listening comprehension in context. Applied Linguistics, 7(3): 284.CrossRefGoogle Scholar
Brown, G.Yule, G. (1983) Discourse analysis. Cambridge: Cambridge Univ Press.CrossRefGoogle Scholar
Brusk, J., Wik, P.Hjalmarsson, A. (2007) DEAL: A Serious Game for CALL Practicing Conversational Skills in the Trade Domain. In: The Proceedings of SlaTE-Workshop on Speech and Language Technology in Education. Pennsylvania, USA.Google Scholar
Bryan, S. (2005) The relationship between negotiated interaction, learner uptake, and lexical acquisition in task-based computer-mediated communication. Tesol Quarterly, 39(1): 33–58.Google Scholar
Burton, R. R. (1976) Semantic grammar: a technique for efficient language understanding in limited domains. PhD Dissertation, University of California, Irvine.Google Scholar
Byrne, D. (1986) Teaching Oral English. Harlow: Longman.Google Scholar
Carroll, D. W. (2003) Psychology of Language (4 ed.). Belmont, CA: Wadsworth Publishing.Google Scholar
Cowley, S. J.MacDorman, K. (1995) Simulating convesations: The communion game. AI & Society, 9(2): 116–137.Google Scholar
Cronbach, L. J. (1951) Coefficient alpha and the internal structure of tests. Psychometrika, 16(3): 297–334.CrossRefGoogle Scholar
Dalby, J.Kewley-Port, D. (2005) Explicit pronunciation training using automatic speech recognition technology. In: Yong, Z. (ed.), Research in technology and second language education: developments and directions. Connecticut: Information Age Publishing, 379.Google Scholar
Dasarathy, B. V. (1990) Nearest neighbor (NN) norms: NN pattern classification techniques. Los Alamitos: IEEE Computer Society Press.Google Scholar
Friedman, B., Kahn, P. H. Jr.Hagman, J. (2003) Hardware companions?: What online AIBO discussion forums reveal about the human-robotic relationship. In: Proceedings of the SIGCHI conference on Human factors in computing systems, 273–280.Google Scholar
Fujita, M. (2001) AIBO: Toward the era of digital creatures. The International Journal of Robotics Research, 20(10): 781.CrossRefGoogle Scholar
Garrod, S. (1986) Language comprehension in context: A psychological perspective. Applied Linguistics, 7(3): 226.CrossRefGoogle Scholar
Goronzy, S. (2002) Robust adaptation to non-native accents in automatic speech recognition. New York: Springer-Verlag New York Inc.CrossRefGoogle Scholar
Han, J., Jo, M., Park, S.Kim, S. (2005) The educational use of home robots for children. In: IEEE International Workshop on Robot and Human Interactive Communication, 2005. ROMAN 2005, 378–383.Google Scholar
Handley, Z. (2009) Is text-to-speech synthesis ready for use in computer-assisted language learning? Speech Communication, 51(10): 906–919.CrossRefGoogle Scholar
Heift, T.Nicholson, D. (2001) Web delivery of adaptive and interactive language tutoring. International Journal of Artificial Intelligence in Education, 12(4): 310–324.Google Scholar
Heift, T., Schulze, M. (2007) Errors and Intelligence in CALL. Parsers and Pedagogues. New York: Routledge.Google Scholar
Hinde, R. A. (1987) Individuals, relationships & culture: Links between ethology and the social sciences. New York: Cambridge Univ Press.Google Scholar
Johnson, H. (1992) Defossilizing. ELT Journal, 46(2): 180.CrossRefGoogle Scholar
Kanda, T., Hirano, T., Eaton, D.Ishiguro, H. (2004) Interactive robots as social partners and peer tutors for children: A field trial. Human-Computer Interaction, 19(1): 61–84.Google Scholar
Krashen, S. D. (1985) The input hypothesis: Issues and implications. New York: Longman.Google Scholar
Krashen, S. D. (2003) Explorations in language acquisition and use: The Taipei lectures. TESL-EJ, 7(2): 96.Google Scholar
Kutner, M., Nachtsheim, C., Neter, J.Li, W. (2004) Applied Linear Statistical Models (5th ed.). New York: McGraw-Hill/Irwin.Google Scholar
Lafferty, J., McCallum, A.Pereira, F. (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: International Conference on Machine Learning. Williams College, Williamstown, MA, USA, 282–289.Google Scholar
Lai, C., Fei, F.Roots, R. (2008) The Contingency of Recasts and Noticing. CALICO Journal, 26(1): 21.Google Scholar
Lai, C.Zhao, Y. (2006) Noticing and text-based chat. Language Learning & Technology, 10(3): 102–120.Google Scholar
Lee, C., Jung, S., Kim, S.Lee, G. G. (2009) Example-based dialog modeling for practical multi-domain dialog system. Speech Communication, 51(5): 466–484.Google Scholar
Lee, S., Lee, C., Lee, J., Noh, H.Lee, G. G. (2010) Intention-based Corrective Feedback Generation using Context-aware Model. In: Proceedings of International Conference on Computer Supported Education, Valencia, Spain.Google Scholar
Leggetter, C. J.Woodland, P. C. (1995) Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Computer speech and language, 9(2): 171.CrossRefGoogle Scholar
Liang, A.McQueen, R. J. (1999) Computer Assisted Adult Interactive Learning in a Multi-Cultural Environment. Adult Learning, 11(1): 26–29.CrossRefGoogle Scholar
Loewen, S.Erlam, R. (2006) Corrective feedback in the chatroom: An experimental study. Computer Assisted Language Learning, 19(1): 1–14.CrossRefGoogle Scholar
Long, M. H. (2000) Focus on form in task-based language teaching. In: Language policy and pedagogy: Essays in honor of A. Ronald Walton. Amsterdam: John Benjamins Publishing Company, 179–192.CrossRefGoogle Scholar
Masgoret, A. M.Gardner, R. C. (2003) Attitudes, Motivation, and Second Language Learning: A Meta-Analysis of Studies Conducted by Gardner and Associates. Language Learning, 53(S1): 167–210.Google Scholar
Morton, H.Jack, M. A. (2005) Scenario-based spoken interaction with virtual agents. Computer Assisted Language Learning, 18(3): 171–191.CrossRefGoogle Scholar
Nagata, N. (2002) BANZAI: An application of natural language processing to web-based language learning. CALICO Journal, 19(3): 583–600.CrossRefGoogle Scholar
Neri, A., Cucchiarini, C.Strik, H. (2001) Effective feedback on L2 pronunciation in ASR-based CALL. In: Proceedings of the workshop on Computer Assisted Language Learning, Artificial Intelligence in Education Conference. San Antonio, Texas, 40–48.Google Scholar
Oh, Y. R., Yoon, J. S.Kim, H. K. (2007) Acoustic model adaptation based on pronunciation variability analysis for non-native speech recognition. Speech Communication, 49(1): 59–70.CrossRefGoogle Scholar
Paul, D. B.Baker, J. M. (1992) The design for the Wall Street Journal-based CSR corpus. In: Proceedings of the workshop on Speech and Natural Language. Harriman, NY, 357–362.Google Scholar
Petersen, K. A. (2010) Implicit Corrective Feedback in Computer-Guided Interaction: Does Mode Matter? PhD Disserton, Georgetown University.Google Scholar
Ramshaw, L. A.Marcus, M. P. (1995) Text chunking using transformation-based learning. In: Proceedings of the Third ACL Workshop on Very Large Corpora. Cambridge, Massachusetts, USA: MIT, 82–94.Google Scholar
Ratnaparkhi, A. (1998) Maximum entropy models for natural language ambiguity resolution. PhD thesis, University of Pennsylvania.Google Scholar
Raux, A.Eskenazi, M. (2004) Using task-oriented spoken dialogue systems for language learning: potential, practical applications and challenges. In: InSTIL/ICALL Symposium, Venice, Italy, 2004.Google Scholar
Roed, J. (2003) Language learner behaviour in a virtual environment. Computer Assisted Language Learning, 16(2): 155–172.CrossRefGoogle Scholar
Rosnow, R. L.Rosenthal, R. (2007) Beginning behavioral research: A conceptual primer (6 ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
Sachs, R.Suh, B. (2007) Textually enhanced recasts, learner awareness, and L2 outcomes in synchronous computer-mediated interaction. In: Conversational interaction in second language acquisition: A collection of empirical studies. Oxford: Oxford University Press, 197–227.Google Scholar
Schmidt, R. W. (2001) Attention. In: Cognition and second language instruction. Cambridge: Cambridge University Press, 3–32.CrossRefGoogle Scholar
Schneider, D.McCoy, K. F. (1998) Recognizing syntactic errors in the writing of second language learners. In: Proceedings of the 17th international conference on Computational linguistics-Volume 2. Montreal, Canada, 1198–1204.Google Scholar
Seneff, S., Wang, C.Zhang, J. (2004) Spoken conversational interaction for language learning. In: InSTIL/ICALL Symposium, Venice, Italy, 2004.Google Scholar
Shieber, S. M. (1986) An introduction to unification-based approaches to grammar. CSLI Lecture Notes No. 4, CSLI, Stanford.Google Scholar
Smith, B. (2004) Computer-mediated negotiated interaction and lexical acquisition. Studies in Second Language Acquisition, 26(03): 365–398.CrossRefGoogle Scholar
Swain, M. (1985) Communicative competence: Some roles of comprehensible input and comprehensible output in its development. Input in second language acquisition, 15: 165–179.Google Scholar
Wada, K., Shibata, T., Saito, T.Tanie, K. (2002) Analysis of factors that bring mental effects to elderly people in robot assisted activity. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002. (Vol. 2:1152–1157).Google Scholar
Yi, H.Majima, J. (1993) The teacher-learner relationship and classroom interaction in distance learning: A case study of the Japanese language classes at an American high school. Foreign Language Annals, 26(1): 21–30.CrossRefGoogle Scholar
Zavaliagkos, G., Schwartz, R.McDonough, J. (1996) Maximum a posteriori adaptation for large scale HMM recognizers. In: IEEE International Conference on Acoustics, Speech and Signal Processing. Atlanta, Georgia, 725–728.Google Scholar