Autonomous Robots

, Volume 25, Issue 4, pp 367–382 | Cite as

An autonomous educational mobile robot mediator

Article

Abstract

So far, most of the applications of robotic technology to education have mainly focused on supporting the teaching of subjects that are closely related to the Robotics field, such as robot programming, robot construction, or mechatronics. Moreover, most of the applications have used the robot as an end or a passive tool of the learning activity, where the robot has been constructed or programmed. In this paper, we present a novel application of robotic technologies to education, where we use the real world situatedness of a robot to teach non-robotic related subjects, such as math and physics. Furthermore, we also provide the robot with a suitable degree of autonomy to actively guide and mediate in the development of the educational activity. We present our approach as an educational framework based on a collaborative and constructivist learning environment, where the robot is able to act as an interaction mediator capable of managing the interactions occurring among the working students. We illustrate the use of this framework by a 4-step methodology that is used to implement two educational activities. These activities were tested at local schools with encouraging results. Accordingly, the main contributions of this work are: i) A novel use of a mobile robot to illustrate and teach relevant concepts and properties of the real world; ii) A novel use of robots as mediators that autonomously guide an educational activity using a collaborative and constructivist learning approach; iii) The implementation and testing of these ideas in a real scenario, working with students at local schools.

Keywords

Robots in education Mobile robots Autonomous robot Robot-human interaction 

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References

  1. Ahlgren, D., & Verner, I. (2002). An international view of robotics as an educational medium. In Int. conf. on engineering education. Google Scholar
  2. Arons, A. B. (1990). A guide to introductory physics teaching. New York: Wiley. Google Scholar
  3. Avanzato, R. (2002). Mobile robot navigation contest for undergraduate design and k-12 outreach. In Proc. of conf. of American society for engineering education (ASEE). Google Scholar
  4. Beichner, R. J. (1994). Testing student interpretation of kinematics graphs. American Journal of Physics, 62(8), 750–762. CrossRefGoogle Scholar
  5. Burgard, W., Cremers, A. B., Fox, D., Hahnel, D., Lakemeyer, G., Schulz, D., Steiner, W., & Thrun, S. (1999). Experiences with an interactive museum tour-guide robot. Artificial Intelligence, 114(1–2), 3–55. MATHCrossRefGoogle Scholar
  6. Carbonell, J. R. (1970). AI in CAI: An artificial-intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems, 11(4), 190–202. CrossRefGoogle Scholar
  7. Clements, D. H. (1999). Teaching length measurement: Research challenges. School Science and Mathematics, 99(1), 5–11. MathSciNetCrossRefGoogle Scholar
  8. Dillenbourg, P. (1999). Collaborative learning: cognitive and computational approaches. Oxford: Pergamon. Google Scholar
  9. Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003). A survey of socially interactive robots. Robotics and Autonomous Systems, 42(3), 143–166. MATHCrossRefGoogle Scholar
  10. Gockley, R., Bruce, A., Forlizzi, J., Michalowski, M. P., Mundell, A., Rosenthal, S., Sellner, B. P., Simmons, R., Snipes, K., Schultz, A., & Wang, J. (2005). Designing robots for long-term social interaction. In Proc. of IEEE/RSJ int. conf. on intelligent robots and systems (IROS) (pp. 2199–2204). Google Scholar
  11. Harakiewicz, J. M., Barron, K. E., Tauer, J. M., Carter, S. M., & Elliot, A. J. (2000). Short-term and long-term consequences of achievement goals in college: Predicting continued interest and performance over time. Journal of Educational Psychology, 92, 316–330. CrossRefGoogle Scholar
  12. Hartley, J., & Sleeman, D. H. (1973). Towards more intelligent teaching systems. International Journal of Man-Machine Studies, 5, 215–236. CrossRefGoogle Scholar
  13. Hidi, S. (2000). An interest researcher’s perspective on the effects of extrinsic and intrinsic factors on motivation. In Intrinsic motivation: controversies and new directions (pp. 309–339). Google Scholar
  14. Ihlenfeldt, W. D. (1997). Virtual reality in chemistry. Journal of Molecular Modeling, 3(9), 386–402. CrossRefGoogle Scholar
  15. Jansen, M., Oelinger, M., Hoeksema, K., & Hoppe, U. (2004). An interactive maze scenario with physical robots and other smart devices. In Proc. of 2nd IEEE int. workshop on wireless and mobile technologies in education (WMTE’04). Google Scholar
  16. Jermann, P., Soller, A., & Muehlenbrock, M. (2001). From mirroring to guiding: A review of the state of the art technology for supporting collaborative learning. In European perspectives on computer-supported collaborative learning, EuroCSCL (pp. 324–331). Google Scholar
  17. Johnson, D. W., & Johnson, R. (1999). Learning together and alone: cooperative, competitive, and individualistic learning (5th ed.). Englewood Cliffs: Prentice-Hall. Google Scholar
  18. Klassner, F., & Andreson, S. (2003). Lego mindstorms: not just for k-12 anymore. IEEE Robotics and Automation Magazine, 10(2), 12–18. CrossRefGoogle Scholar
  19. Lalonde, J., Bartley, C., & Nourbakhsh, I. (2006). Mobile robot programming in education. In IEEE int. conf. on robotics and automation (ICRA). Google Scholar
  20. Leinhardt, G., Zaslavsky, O., & Stein, M. K. (1990). Functions, graphs, and graphing: Task, learning, and teaching. Review of Educational Research, 60(1), 1–64. Google Scholar
  21. Linn, M. C., Layman, J. W., & Nachmias, R. (1987). Cognitive consequences of microcomputer-based laboratories: graphing skills development. Contemporary Educational Psychology, 12(3), 244–253. CrossRefGoogle Scholar
  22. McDermott, L. C. (1984). Research on conceptual understanding in mechanics. Physics Today, 37(7), 24–32. CrossRefGoogle Scholar
  23. McDermott, L. C. (1991). Millikan lecture 1990: What we teach and what is learned—closing the gap. American Journal of Physics, 59(4), 301–315. CrossRefGoogle Scholar
  24. McDermott, L. C., Rosenquist, M. L., & Van Zee, E. H. (1987). Student difficulties in connecting graphs and physics: Examples from kinematics. American Journal of Physics, 55(6), 503–513. CrossRefGoogle Scholar
  25. Mikropoulos, T., Katsikis, A., Nikolow, E., & Tsakalis, P. (2003). Virtual environments in biology teaching. Journal of Biological Education, 37(4), 176–181. Google Scholar
  26. Mitnik, R. (2008). The robot as an autonomous mediator of the learning experience and the social interactions. PhD thesis, Dept. of Computer Science, Pontificia Universidad Catolica de Chile. Google Scholar
  27. Mitnik, R., Nussbaum, M., & Soto, A. (2004). Mobile robotic supported collaborative learning (MRSCL). In Lecture Notes in Artificial Intelligence (Vol. 3315, p. 912–921). Berlin: Springer. Google Scholar
  28. Murphy, R. (2001). Competing for a robotics education. IEEE Robotics & Automation Society Magazine, June, 44–55. Google Scholar
  29. Murray, T. (1999). Authoring intelligent tutoring systems: An analysis of the state of the art. International Journal of Artificial Intelligence in Education, 10, 98–129. Google Scholar
  30. Nourbakhsh, I., Bobenage, J., Grange, S., Lutz, R., Meyer, R., & Soto, A. (1999). An affective mobile educator with a full-time job. Artificial Intelligence, 114(1–2), 95–124. MATHCrossRefGoogle Scholar
  31. Nourbakhsh, I., Kunz, C., & Willeke, T. (2003). The mobot museum robot installations: A five year experiment. In Proc. of IEEE/RSJ int. conf. on intelligent robots and systems (IROS) (pp. 3636–3641). Google Scholar
  32. Nourbakhsh, I., Hammer, E., Crowley, K., & Wilkinson, K. (2004). Formal measures of learning in a secondary school mobile robotics contest. In IEEE int. conf. on robotics and automation (ICRA). Google Scholar
  33. Papert, S. (1980). Mindstorms: children, computers, and powerful ideas. New York: Basic Books. Google Scholar
  34. Petre, M., & Price, B. (2004). Using robotics to motivate ‘back door’ learning. Education and Information Technologies, 9(2), 147–158. CrossRefGoogle Scholar
  35. Piaget, J., Brown, T. A., Kaegi, C. E., & Rosenzweig, M. R. (1981). Intelligence and affectivity. Their relationship during child development (Annual reviews monograph). Google Scholar
  36. Pineau, J., Montemerlo, M., Pollack, M., Roy, N., & Thrun, S. (2003). Towards robotic assistants in nursing homes: Challenges and results. Robotics and Autonomous Systems, 42(3–4), 271–281. MATHCrossRefGoogle Scholar
  37. Rosenblatt, M., & Choset, H. (2000). Designing and implementing hands-on robotics labs. IEEE Intelligent Systems and their Applications, 15(6), 32–39. CrossRefGoogle Scholar
  38. Rourk, W. (2000). Virtual biochemistry—a case study. Future Generation Computer Systems, 17, 7–14. CrossRefGoogle Scholar
  39. Schroeder, D. V., & Moore, T. A. (1993). A computer-simulated Stern-Gerlach laboratory. American Journal of Physics, 61, 798–805. CrossRefGoogle Scholar
  40. Soto, A., Espinace, P., & Mitnik, R. (2006). A mobile robotics course for undergraduate students in computer science. In Proc. of IEEE Latin American robotics symposium (LARS) (pp. 187–192). Google Scholar
  41. Stein, C. (2002). Botball—Autonomous students engineering autonomous robots. In Proc. of conf. of American society for engineering education (ASEE). Google Scholar
  42. Tao, P. K. (1997). Confronting students alternative conceptions in mechanics with the force and motion microworld. Computers in Physics, 11(2), 199–207. CrossRefGoogle Scholar
  43. Thrun, S., Bennewitz, M., Burgard, W., Cremers, A. B., Dellaert, F., Fox, D., Haehnel, D., Rosenberg, C., Roy, N., Schulte, J., & Schulz, D. (1999). Minerva: A second generation mobile tour-guide robot. In Proc. of the IEEE int. conf. on robotics and automation (ICRA) (pp. 1999–2005). Google Scholar
  44. Trowbridge, D. E., & McDermott, L. C. (1980). Investigation of student understanding of the concept of velocity in one dimension. American Journal of Physics, 48(12), 1020–1028. CrossRefGoogle Scholar
  45. Wang, E., & Wang, R. (2001). Using legos and robolab (LabVIEW) with elementary school children. In 31st conf. on frontiers in education (Vol. 1, pp. T2E–T11). Google Scholar
  46. Weinberg, J., Engel, G., Gu, K., Karacal, C., Smith, S., White, W., & Yu, X. (2001). A multidisciplinary model for using robotics in engineering education. In Proc. of conf. of American society for engineering education (ASEE). Google Scholar
  47. Zurita, G., & Nussbaum, M. (2004). Computer supported collaborative learning using wirelessly interconnected handheld computers. Computers & Education, 42(3), 289–314. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Computer SciencePontificia Universidad Catolica de ChileSantiagoChile

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