skip to main content
10.1145/2696454.2696457acmconferencesArticle/Chapter ViewAbstractPublication PageshriConference Proceedingsconference-collections
research-article

The Robot Who Tried Too Hard: Social Behaviour of a Robot Tutor Can Negatively Affect Child Learning

Published:02 March 2015Publication History

ABSTRACT

Social robots are finding increasing application in the domain of education, particularly for children, to support and augment learning opportunities. With an implicit assumption that social and adaptive behaviour is desirable, it is therefore of interest to determine precisely how these aspects of behaviour may be exploited in robots to support children in their learning. In this paper, we explore this issue by evaluating the effect of a social robot tutoring strategy with children learning about prime numbers. It is shown that the tutoring strategy itself leads to improvement, but that the presence of a robot employing this strategy amplifies this effect, resulting in significant learning. However, it was also found that children interacting with a robot using social and adaptive behaviours in addition to the teaching strategy did not learn a significant amount. These results indicate that while the presence of a physical robot leads to improved learning, caution is required when applying social behaviour to a robot in a tutoring context.

Skip Supplemental Material Section

Supplemental Material

hrifp1064-file3.mp4

mp4

40.1 MB

References

  1. R. K. Atkinson, R. E. Mayer, and M. M. Merrill. Fostering social agency in multimedia learning: Examining the impact of an animated agent's voice. Contemporary Educational Psychology, 30(1):117--139, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  2. P. Baxter, R. Wood, and T. Belpaeme. A touchscreen-based 'sandtray' to facilitate, mediate and contextualise human-robot social interaction. In Proc. HRI'12, pages 105--106, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. T. Belpaeme, P. Baxter, R. Read, R. Wood, H. Cuayahuitl, B. Kiefer, et al. Multimodal child-robot interaction: Building social bonds. Journal of Human-Robot Interaction, 1(2):33--53, 2012.Google ScholarGoogle Scholar
  4. O. A. Blanson Henkemans, B. P. Bierman, J. Janssen, M. A. Neerincx, R. Looije, H. van der Bosch, et al. Using a robot to personalise health education for children with diabetes type 1: A pilot study. Patient Education and Counseling, 92(2):174--181, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  5. B. S. Bloom. The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, pages 4--16, 1984.Google ScholarGoogle Scholar
  6. J. Han, M. Jo, S. Park, and S. Kim. The educational use of home robots for children. In IEEE RO-MAN'05, pages 378--383, 2005.Google ScholarGoogle Scholar
  7. T. Kanda, T. Hirano, D. Eaton, and H. Ishiguro. Interactive robots as social partners and peer tutors for children: A field trial. Human-Computer Interaction, 19(1):61--84, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. Kennedy, P. Baxter, and T. Belpaeme. Constraining content in mediated unstructured social interactions: Studies in the wild. In Proc. AFFINE'13, at ACII'13, pages 728--733, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Kennedy, P. Baxter, and T. Belpaeme. Children comply with a robot's indirect requests. In Proc. HRI'14, pages 198--199, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Kennedy, P. Baxter, and T. Belpaeme. Comparing robot embodiments in a guided discovery learning interaction with children. International Journal of Social Robotics, accepted.Google ScholarGoogle Scholar
  11. H. Kose-Bagci, E. Ferrari, K. Dautenhahn, D. S. Syrdal, and C. L. Nehaniv. Effects of embodiment and gestures on social interaction in drumming games with a humanoid robot. Advanced Robotics, 23(14):1951--1996, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  12. J. R. Landis and G. G. Koch. The measurement of observer agreement for categorical data. Biometrics, 33(1):159--174, 1977.Google ScholarGoogle ScholarCross RefCross Ref
  13. D. Leyzberg, S. Spaulding, and B. Scassellati. Personalizing robot tutors to individual learning differences. In Proc. HRI'14, pages 423--430, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. D. Leyzberg, S. Spaulding, M. Toneva, and B. Scassellati. The physical presence of a robot tutor increases cognitive learning gains. In Proc. CogSci'12, pages 1882--1887, 2012.Google ScholarGoogle Scholar
  15. R. E. Mayer, S. Fennell, L. Farmer, and J. Campbell. A personalization effect in multimedia learning: Students learn better when words are in conversational style rather than formal style. Journal of Educational Psychology, 96(2):389, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  16. L. Moshkina, S. Trickett, and J. G. Trafton. Social engagement in public places: a tale of one robot. In Proc. HRI'14, pages 382--389, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. M. E. O'Neill. The genuine sieve of eratosthenes. Journal of Functional Programming, 19(01):95--106, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. M. Saerbeck, T. Schut, C. Bartneck, and M. D. Janse. Expressive robots in education: Varying the degree of social supportive behavior of a robotic tutor. In Proc. CHI'10, pages 1613--1622, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. P. Schermerhorn, M. Scheutz, and C. R. Crowell. Robot social presence and gender: Do females view robots differently than males? In Proc. HRI'08, pages 263--270, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. E. Short, K. Swift-Spong, J. Greczek, A. Ramachandran, A. Litoiu, E. C. Grigore, et al. How to train your dragonbot: Socially assistive robots for teaching children about nutrition through play. In IEEE RO-MAN'14, pages 924--929, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  21. J. Sweller. Cognitive load theory, learning difficulty, and instructional design. Learning and instruction, 4(4):295--312, 1994.Google ScholarGoogle ScholarCross RefCross Ref
  22. D. Szafir and B. Mutlu. Pay attention!: Designing adaptive agents that monitor and improve user engagement. In Proc. CHI'12, pages 11--20, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. K. VanLehn. The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4):197--221, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  24. K. VanLehn, S. Siler, C. Murray, T. Yamauchi, and W. B. Baggett. Why do only some events cause learning during human tutoring? Cognition and Instruction, 21(3):209--249, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  25. L. S. Vygotsky. Mind in society: The development of higher psychological processes. Harvard university press, 1980.Google ScholarGoogle ScholarCross RefCross Ref
  26. J. Zaki. Cue integration a common framework for social cognition and physical perception. Perspectives on Psychological Science, 8(3):296--312, 2013.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. The Robot Who Tried Too Hard: Social Behaviour of a Robot Tutor Can Negatively Affect Child Learning

    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
      HRI '15: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction
      March 2015
      368 pages
      ISBN:9781450328838
      DOI:10.1145/2696454

      Copyright © 2015 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: 2 March 2015

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      HRI '15 Paper Acceptance Rate43of169submissions,25%Overall Acceptance Rate242of1,000submissions,24%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader