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Expressive robots in education: varying the degree of social supportive behavior of a robotic tutor

Published:10 April 2010Publication History

ABSTRACT

Teaching is inherently a social interaction between teacher and student. Despite this knowledge, many educational tools, such as vocabulary training programs, still model the interaction in a tutoring scenario as unidirectional knowledge transfer rather than a social dialog. Therefore, ongoing research aims to develop virtual agents as more appropriate media in education. Virtual agents can induce the perception of a life-like social interaction partner that communicates through natural modalities such as speech, gestures and emotional expressions. This effect can be additionally enhanced with a physical robotic embodiment.

This paper presents the development of social supportive behaviors for a robotic tutor to be used in a language learning application. The effect of these behaviors on the learning performance of students was evaluated. The results support that employing social supportive behavior increases learning efficiency of students.

References

  1. J. Alderson, C. Clapham, and D. Wall. Language test construction and evaluation. Cambridge University Press, 1995.Google ScholarGoogle Scholar
  2. M. Barrera, I. Sandler, and T. Ramsay. Preliminary development of a scale of social support: Studies on college students. American Journal of Community Psychology, 9(4):435--447, 1981.Google ScholarGoogle ScholarCross RefCross Ref
  3. C. Bartneck, J. Reichenbach, and A. van Breemen. In your face, robot! The influence of a character's embodiment on how users perceive its emotional expressions. In Proceedings of the Design and Emotion 2004 Conference, Ankara, Turkey, pages 32--51, 2004.Google ScholarGoogle Scholar
  4. C. Bartneck, T. Suzuki, T. Kanda, and T. Nomura. The influence of people's culture and prior experiences with AIBO on their attitude towards robots. AI & Society The Journal of Human-Centred Systems, 21(1), 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. A.v. Breemen. Bringing robots to life: Applying principles of animation to robots. In Proceedings of the Workshop on Shaping Human-Robot Interaction -- Understanding the Social Aspects of Intelligent Robotic Products. In Cooperation with the CHI2004 Conference, Vienna, Apr. 2004.Google ScholarGoogle Scholar
  6. J. Brophy. Research linking teacher behavior to student achievement: Potential implications for instruction of chapter 1 students. In Designs for Compensatory Education: Conference Proceedings and Papers, page 60p., 1986.Google ScholarGoogle Scholar
  7. J. Casper and R. Murphy. Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 33(3):367--385, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. L. Darling-Hammond. Teacher quality and student achievement:A review of state policy evidence. Education Policy Analysis Archives, 8(1):1--48, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  9. K. Dautenhahn. Socially intelligent robots: Dimensions of human-robot interaction. Philos Trans R Soc Lond B Biol Sci., 362(1480):679--704, Apr. 2007.Google ScholarGoogle ScholarCross RefCross Ref
  10. D. Dehn and S. van Mulken. The impact of animated interface agents: a review of empirical research. International Journal of Human-Computers Studies, 52(1):1--22, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. Feil-Seifer and M. Mataric. Defining socially assistive robotics. In Rehabilitation Robotics, 2005. ICORR 2005. 9th International Conference on, pages 465--468, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  12. T. Fong, I. Nourbakhsh, and K. Dautenhahn. A survey of socially interactive robots. Robotics and Autonomous Systems, 42(3-4):143--166, March 2003.Google ScholarGoogle ScholarCross RefCross Ref
  13. D. Goldhaber. The mystery of good teaching. Education Next, 2(1):50--55, 2002.Google ScholarGoogle Scholar
  14. M.A. Goodrich, T.W. McLain, J.D. Anderson, J. Sun, and J.W. Crandall. Managing autonomy in robot teams: Observations from four experiments. In HRI, pages 25--32, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. M.A. Goodrich and A.C. Schultz. Human-robot interaction: A survey. Found. Trends Hum.-Comput. Interact., 1(3):203--275, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. P. Hardre. Beyond two decades of motivation: A review of the research and practice in instructional design and human performance technology. Human Resource Development Review, 2(1):54, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  17. C. Hongpaisanwiwat and M. Lewis. The effects of animated character in multimedia presentation: Attention and comprehension. In Systems, Man and Cybernetics, 2003. IEEE International Conference on, volume 2, pages 1350--1352, Oct. 2003.Google ScholarGoogle ScholarCross RefCross Ref
  18. R. Jackson and E. Fagan. Collaboration and learning within immersive virtual reality. In Proceedings of the third international conference on Collaborative virtual environments, pages 83--92. ACM New York, NY, USA, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. A. Jasman. The role of teacher educators in the promotion, support and assurance of teacher quality. http://www.atea.schools.net.au/atea/papers/jasman.pdf, 2001.Google ScholarGoogle Scholar
  20. W. Johnson, J. Rickel, and J. Lester. Animated pedagogical agents: Face-to-face interaction in interactive learning environments. International Journal of Artificial Intelligence in Education, 11(1):47--78, 2000.Google ScholarGoogle Scholar
  21. W. Johnson, P. Rizzo, W. Bosma, S. Kole, M. Ghijsen, and H. Van Welbergen. Generating socially appropriate tutorial dialog. Lecture Notes in Computer Science, 3068:254--264, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  22. S.E. Kisa. Toki pona. Online source: http://www.tokipona.org (accessed Jan. 2009), 2007.Google ScholarGoogle Scholar
  23. C. Langford, J. Bowsher, J. Maloney, and P. Lillis. Social support: A conceptual analysis. J Adv Nurs, 25(1):95--100, 1997.Google ScholarGoogle ScholarCross RefCross Ref
  24. Lego. Mindstorms NXT. Online source: http://mindstorms.lego.com, 2007.Google ScholarGoogle Scholar
  25. J.C. Lester, S.A. Converse, S.E. Kahler, S.T. Barlow, B.A. Stone, and R. S. Bhogal. The persona effect: Affective impact of animated pedagogical agents. In CHI '97: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 359--366, New York, NY, USA, 1997. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. S.P. Linder, B.E. Nestrick, S. Mulders, and C.L. Lavelle. Facilitating active learning with inexpensive mobile robots. Journal of Computing Sciences in Colleges, 16(4):21--33, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. E. McAuley. Psychometric Properties of the Intrinsic Motivation Inventory in a Competitive Sport Setting: A Confirmatory Factor Analysis. Research Quarterly for Exercise and Sport, 60(1):48--58, 1989.Google ScholarGoogle ScholarCross RefCross Ref
  28. R. Moreno, R. Mayer, H. Spires, and J. Lester. The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction, pages 177--213, 2001.Google ScholarGoogle Scholar
  29. O. Mubin, S. Shahid, C. Bartneck, E. Krahmer, M. Swerts, and L. Feijs. Using language tests and emotional expressions to determine the learnability of artificial languages. In CHI 2009 Conference on Human Factors in Computing Systems, pages 4075--4080, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. S. Parise, S. Kiesler, L. Sproull, and K. Waters. Cooperating with life-like interface agents. Computers in Human Behavior, 15(2):123--142, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  31. A. Powers, S. Kiesler, S. Fussell, and C. Torrey. Comparing a computer agent with a humanoid robot. In Proceeding of the ACM/IEEE international conference on Human-robot interaction, pages 145--152, New York, NY, USA, 2007. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. B. Reeves and C. Nass. The Media Equation : How People Treat Computers, Television, and New Media like Real People and Places (CSLI Lecture Notes (Hardcover)). The Center for the Study of Language and Information Publications, September 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. M. Saerbeck and A.J. van Breemen. Design guidelines and tools for creating believable motion for personal robots. In Robot and Human interactive Communication, 2007. RO-MAN 2007. The 16th IEEE International Symposium on, pages 386--391, 2007.Google ScholarGoogle Scholar
  34. N. Shin and S. Kim. Learning about, from, and with Robots: Students' Perspectives. In Robot and Human interactive Communication, 2007. RO-MAN 2007. The 16th IEEE International Symposium on, pages 1040--1045, 2007.Google ScholarGoogle Scholar
  35. K. Shinozawa, F. Naya, J. Yamato, and K. Kogure. Differences in effect of robot and screen agent recommendations on human decision-making. International Journal of Human-Computer Studies, 62(2):267--279, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. E. Strommen and K. Alexander. Emotional interfaces for interactive aardvarks: designing affect into social interfaces for children. In Proceedings of the SIGCHI conference on Human factors in computing systems: the CHI is the limit, pages 528--535. ACM New York, NY, USA, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. R. Tiberius. The why of teacher/student relationships. Essays on teaching excellence, Professional and Organizational Development (POD) reading packet 10(4), 1993.Google ScholarGoogle Scholar
  38. R. Tiberius and J. Billson. The social context of teaching and learning. New Directions for Teaching and Learning, 45:67--86, 1991.Google ScholarGoogle ScholarCross RefCross Ref
  39. X. Tu. Artificial Animals for Computer Animation: Biomechanics, Locomotion, Perception, and Behavior, volume 1635/1999 of Lecture Notes in Computer Science. Springer Berlin, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. A. van Breemen and Y. Xue. Advanced animation engine for user-interface robots. In Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on, pages 1824--1830. IEEE CNF, Oct 2006.Google ScholarGoogle ScholarCross RefCross Ref
  41. A. Wayne and P. Youngs. Teacher characteristics and student achievement gains: A review. Review of Educational Research, 73(1):89, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  42. J. Yamato, R. Brooks, K. Shinozawa, and F. Naya. Human-robot dynamic social interaction. NTT Tech Rev., 1(6):37--43, 2003.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Conferences
      CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
      April 2010
      2690 pages
      ISBN:9781605589299
      DOI:10.1145/1753326

      Copyright © 2010 ACM

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      Publication History

      • Published: 10 April 2010

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