skip to main content
10.1145/3406499.3415065acmconferencesArticle/Chapter ViewAbstractPublication PageshaiConference Proceedingsconference-collections
research-article

Customized Robot-Assisted Language Learning to Support Immigrants at Work: Findings and Insights from a Qualitative User Experience Study

Authors Info & Claims
Published:10 November 2020Publication History

ABSTRACT

There is a strong societal pressure for the immigrants to learn the local language to be able to properly enter the work-life and social life in the new culture. The limit of resources in second language teaching and the challenges related to language practicing with native people, encourages us to search for alternative options for the second language learning. Robot-assisted language learning (RALL) provides one potential option. In this article, we focus on exploring the potentials of customized language robots for immigrants to support their professional development and adaptation to the local work contexts. We describe our qualitative and exploratory user study with 10 immigrants as participants, focusing on their user experience (UX) of the customized RALL solution for language learning at work settings. We collected data through 1) field observations of the authentic RALL sessions in a user pilot, and 2) user interviews in the end of the pilot. The research shows that customization of several aspects of RALL is needed to support immigrants? language learning at work. These include, e.g. the vocabulary and tasks, the level, the speed of language, and the timing of the RALL. The optimal customization can be achieved by co-designing the RALL within the multidisciplinary team including the workplace representatives, language trainer and programmer. The RALL provides a lot of potential for immigrants? language learning as it offers unique learning experiences and many possibilities to keep up the learner?s attention and motivation. However, there are also challenging aspects, which need further consideration.

References

  1. Ahtinen, A. & Kaipainen, K. (2020). Learning and Teaching Experiences with a Persuasive Social Robot in Primary School -- Findings and Implications from a 4-Month Field Study. Persuasive Technology 2020.Google ScholarGoogle Scholar
  2. Bartneck, C., Forlizzi, J.: A design-centred framework for social human-robot interaction. 13th IEEE International Workshop on Robot and Human Interactive Communication 2004, pp. 591--594. IEEE..Google ScholarGoogle ScholarCross RefCross Ref
  3. Belpaeme, T., Kennedy, J., Ramachandran, A. et al.: Social robots for education: A review. Science Robotics, 3(21), eaat5954 (2018).Google ScholarGoogle Scholar
  4. Belpaeme, T., Vogt, P., Van den Berghe, R., et al. (2018). Guidelines for designing social robots as second language tutors. International Journal of Social Robotics, 10(3), 325--341.Google ScholarGoogle ScholarCross RefCross Ref
  5. Broadbent, E., Tamagawa, R., Kerse, N. et al. (2009, September). Retirement home staff and residents' preferences for healthcare robots. In RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication (pp. 645--650). IEEE..Google ScholarGoogle Scholar
  6. Bruno, B., Chong, N. Y., Kamide, H., et al. (2017, August). Paving the way for culturally competent robots: A position paper. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (pp. 553--560). IEEE.Google ScholarGoogle Scholar
  7. Carolis, B. D., Palestra, G., Penna, C. D. et al. (2019). Social robots supporting the inclusion of unaccompanied migrant children: Teaching the meaning of culture-related gestures. Je-LKS : Journal of E-Learning and Knowledge Society, 15(2) doi:10.20368/1971-8829/1636..Google ScholarGoogle Scholar
  8. Engwall, O., Lopes, J., & Åhlund, A. (2020). Robot interaction styles for conversation practice in second language learning. International Journal of Social Robotics, 1--26.Google ScholarGoogle Scholar
  9. Kennedy, J., Lemaignan, S., Montassier, C. et al..: Child speech recognition in human-robot interaction: evaluations and recommendations. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 82--90 (2017).Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Levy M. (1997) CALL: context and conceptualisation, Oxford: Oxford University Press.Google ScholarGoogle Scholar
  11. Leyzberg, D., Spaulding, S., Toneva, M., & Scassellati, B. (2012). The physical presence of a robot tutor increases cognitive learning gains. In Proceedings of the annual meeting of the cognitive science society (Vol. 34, No. 34)...Google ScholarGoogle Scholar
  12. Lopes, J., Engwall, O., Skantze, G. (2017) A First Visit to the Robot Language Café. In: Engwall, Lopes (ed.), Proceedings of the ISCA workshop on Speech and Language Technology in Education Stockholm.Google ScholarGoogle ScholarCross RefCross Ref
  13. Lopes, J., Robb, D. A., Ahmad, M., Liu, X., Lohan, K., & Hastie, H. (2019, March). Towards a conversational agent for remote robot-human teaming. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 548--549). IEEE.Google ScholarGoogle Scholar
  14. Nielson, K. B. (2011). Self-study with language learning software in the workplace: What happens?. Language Learning & Technology, 15(3), 110--129.Google ScholarGoogle Scholar
  15. Randall, N. (2019). A Survey of Robot-Assisted Language Learning (RALL). ACM Transactions on Human-Robot Interaction (THRI), 9(1), 1--36.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Šabanović, S. (2010, October). Emotion in robot cultures: Cultural models of affect in social robot design. In Proceedings of the Conference on Design & Emotion.Google ScholarGoogle Scholar
  17. Saukkonen, P. (2016). From fragmentation to integration. Dealing with migration flows in Finland, 18.Google ScholarGoogle Scholar
  18. Simo, H., Avelino, J., Duarte, N., & Figueiredo, R. (2018). GeeBot: A robotic platform for refugee integration. Paper presented at the 365--366. doi:10.1145/3173386.3177833.Google ScholarGoogle Scholar
  19. Tanaka, F., Isshiki, K., Takahashi, F. et al.: Pepper learns together with children: Development of an educational application. In IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 270--275 (2015).Google ScholarGoogle ScholarCross RefCross Ref
  20. Tuna, A., & Tuna, G. (2019). The use of humanoid robots with multilingual interaction skills in teaching a foreign language: opportunities, research challenges and future research directions. CEPS Journal, 9(3), 95--115..Google ScholarGoogle ScholarCross RefCross Ref
  21. van den Berghe, R., Verhagen, J., Oudgenoeg-Paz, O. et al. (2019). Social robots for language learning: A review. Review of Educational Research, 89(2), 259--295.Google ScholarGoogle ScholarCross RefCross Ref
  22. Vogt, P., van den Berghe, R., de Haas, M., Hoffman, L., Kanero, J., Mamus, E., Papadopoulos, F.: Second Language Tutoring Using Social Robots: A Large-Scale Study. In 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 497--505, IEEE (2019).Google ScholarGoogle Scholar
  23. Zaga, C., Lohse, M., Truong, K. P., Evers, V. The effect of a robot's social character on children's task engagement: Peer versus tutor. In International Conference on Social Robotics, pp. 704--713, Springer, Cham (2015)Google ScholarGoogle ScholarCross RefCross Ref

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Conferences
    HAI '20: Proceedings of the 8th International Conference on Human-Agent Interaction
    November 2020
    304 pages
    ISBN:9781450380546
    DOI:10.1145/3406499

    Copyright © 2020 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 10 November 2020

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article

    Acceptance Rates

    Overall Acceptance Rate121of404submissions,30%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader