ABSTRACT
In education research, there is a widely-cited result called "Bloom's two sigma" that characterizes the differences in learning outcomes between students who receive one-on-one tutoring and those who receive traditional classroom instruction. Tutored students scored in the 95th percentile, or two sigmas above the mean, on average, compared to students who received traditional classroom instruction. In human-robot interaction research, however, there is relatively little work exploring the potential benefits of personalizing a robot's actions to an individual's strengths and weaknesses. In this study, participants solved grid-based logic puzzles with the help of a personalized or non-personalized robot tutor. Participants' puzzle solving times were compared between two non-personalized control conditions and two personalized conditions (n=80). Although the robot's personalizations were less sophisticated than what a human tutor can do, we still witnessed a "one-sigma" improvement (68th percentile) in post-tests between treatment and control groups. We present these results as evidence that even relatively simple personalizations can yield significant benefits in educational or assistive human-robot interactions.
- B. S. Bloom, "The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring." Educational Researcher, vol. 13, no. 6, pp. 4--16, 1984.Google ScholarCross Ref
- M. K. Lee, J. Forlizzi, S. B. Kiesler, P. E. Rybski, J. Antanitis, and S. Savetsila, "Personalization in HRI: a longitudinal field experiment." 7th ACM/IEEE International Conference on Human-Robot Interaction, pp. 319--326, 2012. Google ScholarDigital Library
- K. VanLehn, "The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems," Educational Psychologist, vol. 46, no. 4, pp. 197--221, 2011.Google ScholarCross Ref
- R. Nkambou, J. Bourdeau, and V. Psyché, "Building Intelligent Tutoring Systems: An overview," Advances in Intelligent Tutoring Systems, pp. 361--375, 2010.Google ScholarCross Ref
- C. D. Kidd and C. Breazeal, "Robots at home: Understanding long-term human-robot interaction," 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3230--3235, 2008.Google Scholar
- J.-Y. Sung, R. E. Grinter, and H. I. Christensen, "Pimp My Roomba": designing for personalization." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 193--196, 2009. Google ScholarDigital Library
- J. R. Movellan, F. Tanaka, I. R. Fasel, C. Taylor, P. Ruvolo, and M. Eckhardt, "The RUBI project: A progress report," 2nd ACM/IEEE International Conference on Human-Robot Interaction, pp. 333--339, 2007. Google ScholarDigital Library
- E. Hyun, H. Yoon, and S. Son, "Relationships between user experiences and children's perceptions of the education robot," 5th ACM/IEEE International Conference on Human-Robot Interaction, pp. 199--200, 2010. Google ScholarDigital Library
- M. Bennewitz, F. Faber, D. Joho, M. Schreiber, and S. Behnke, "Towards a humanoid museum guide robot that interacts with multiple persons," 5th IEEE-RAS International Conference on Humanoid Robots, pp. 418--423, dec. 2005.Google Scholar
- J. K. Lee, R. Toscano, W. Stiehl, and C. Breazeal, "The design of a semi-autonomous robot avatar for family communication and education," Robot and Human Interactive Communication, 2008. RO-MAN 2008. The 17th IEEE International Symposium on, pp. 166 --173, aug. 2008.Google Scholar
- I. Leite, G. Castellano, A. Pereira, C. Martinho, and A. Paiva, "Long-term interactions with empathic robots: Evaluating perceived support in children," Lecture Notes in Computer Science, vol. 7621, pp. 298--307, 2012. Google ScholarDigital Library
- D. Leyzberg, S. Spaulding, M. Toneva, and B. Scassellati, "The physical presence of a robot tutor increases cognitive learning gains," in Proceedings of the 34th Annual Conference of the Cognitive Science Society. Austin,TX: Cognitive Science Society, 2012.Google Scholar
- H. Kozima, C. Nakagawa, and Y. Yasuda, "Interactive robots for communication-care: A case-study in autism therapy," IEEE International Symposium on Robot and Human Interactive Communication, 2005.Google Scholar
- D. Leyzberg, E. Avrunin, J. Liu, and B. Scassellati, "Robots that express emotion elicit better human teaching," in 6th International Conference on Human-Robot Interaction. New York, NY, USA: ACM, 2011, pp. 347--354. Google ScholarDigital Library
- C.-H. Yu, H.-L. Lee, and L.-H. Chen, "An efficient algorithm for solving nonograms," Applied Intelligence, vol. 35, no. 1, pp. 18--31, 2011. Google ScholarDigital Library
Index Terms
- Personalizing robot tutors to individuals' learning differences
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