Advertisement

Cognitive Tutors as Research Platforms: Extending an Established Tutoring System for Collaborative and Metacognitive Experimentation

  • Erin Walker
  • Kenneth Koedinger
  • Bruce McLaren
  • Nikol Rummel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)

Abstract

Cognitive tutors have been shown to increase student learning in long-term classroom studies but would become even more effective if they provided collaborative support and metacognitive tutoring. Reconceptualizing an established tutoring system as a research platform to test different collaborative and metacognitive interventions would lead to gains in learning research. In this paper, we define a component-based architecture for such a platform, drawing from previous theoretical frameworks for tutoring systems. We then describe two practical implementation challenges not typically addressed by these frameworks. We detail our efforts to extend a cognitive tutor and evaluate our progress in terms of flexibility, control, and practicality.

Keywords

Intelligent Tutor System Research Platform Computer Support Collaborative Learn Cognitive Tutor Metacognitive Experimentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A.: Intelligent Tutoring Goes to School in the Big City. Journal of Artificial Intelligence in Education 8, 30–43 (1997)Google Scholar
  2. 2.
    Morgan, P., Ritter, S.: An experimental study of the effects of Cognitive Tutor® Alegbra I on student knowledge and attitude (2002), Available from Carnegie Learning, Inc., www.carnegielearning.com/research/research_reports/morgan_ritter_2002.pdf
  3. 3.
    Johnson, D.W., Johnson, R.T.: Cooperative learning and achievement. In: Sharan, S. (ed.) Cooperative learning: Theory and research, pp. 23–37. Praeger, NY (1990)Google Scholar
  4. 4.
    Aleven, V., McLaren, B.M., Roll, I., Koedinger, K.R.: Toward Tutoring Help Seeking. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 227–239. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Beck, J.E., Mostow, J., Bey, J.: Can automated questions scaffold children’s reading comprehension? In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 478–490. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Aleven, V., Roll, I., McLaren, B.M., Ryu, E.J., Koedinger, K.R.: An architecture to combine meta-cognitive and cognitive tutoring: Pilot testing the Help Tutor. In: Proceedings of 12th International Conference on Artificial Intelligence in Education (2005)Google Scholar
  7. 7.
    Weinberger, A., Reiserer, M., Ertl, B., Fischer, F., Mandl, H.: Facilitating col-laborative knowledge construction in computer-mediated learning with structuring tools. In: Bromme, R., Hesse, F., Spada, H. (eds.) Barriers and Biases in network-based knowledge communication in groups. Kluwer, Dordrecht (2003)Google Scholar
  8. 8.
    McLaren, B.M., Bollen, L., Walker, E., Harrer, A., Sewall, J.: Cognitive Tutoring of Collaboration: Developmental and Empirical Steps Toward Realization. In: Proceedings of the Conference on Computer Supported Collaborative Learning (2005)Google Scholar
  9. 9.
    Krueger, C.W.: Software reuse. Computing Surveys 24(2), 131–183 (1992)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Roschelle, J., Kaput, J., Stroup, W., Kahn, T.M.: Scaleable integration of educational software: Exploring the promise of component architectures. Journal of Interactive Media in Education 6 (1998)Google Scholar
  11. 11.
    ADL, Sharable Content Object Reference Model 2004 2nd Edition Overview (2004a)Google Scholar
  12. 12.
    Brusilovsky, P.: KnowledgeTree: A distributed architecture for adaptive e-learning. In: Proc. of WWW 2004 - The Thirteen International World Wide Web Conference (2004)Google Scholar
  13. 13.
    Vassileva, J., McCalla, G., Greer, J.: Multi-Agent Multi-User Modeling. User Modeling and User-Adapted Interaction 13(1-2), 179–210 (2003)CrossRefGoogle Scholar
  14. 14.
    Muhlenbrock, M., Tewissen, F., Hoppe, H.U.: A framework system for intelligent support in open distributed learning environments. International Journal of Artificial Intelligence in Education 9, 256–274Google Scholar
  15. 15.
    Ritter, S., Koedinger, K.R.: An architecture for plug-in tutor agents. Journal of Artificial Intelligence in Education 7(3/4), 315–347 (1996)Google Scholar
  16. 16.
    Ritter, S.: Communication, cooperation and competition among multiple tutor agents. In: du Boulay, B., Mizoguchi, R. (eds.) Artificial Intelligence in Education: Knowledge and media in learning systems, pp. 31–38. IOS Press, Amsterdam (1997)Google Scholar
  17. 17.
    Walker, E.: Mutual peer tutoring: A Collaborative Addition to the Cognitive Tutor Algebra I. Accepted as a Young Researcher’s Track paper at the International Conference on Artificial Intelligence and Education (2005)Google Scholar
  18. 18.
    McLaren, B.M., Walker, E., Koedinger, K., Rummel, N., Spada, H., Kalchman, M.: Improving Algebra Learning and Collaboration through Collaborative Extensions to the Algebra Cognitive Tutor., Poster Presented at CSCL 2005, Taipei, Taiwan (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Erin Walker
    • 1
  • Kenneth Koedinger
    • 1
  • Bruce McLaren
    • 1
  • Nikol Rummel
    • 2
  1. 1.Human Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of PsychologyAlbert-Ludwigs-Universitat FreiburgGermany

Personalised recommendations