Andes: A Coached Problem Solving Environment for Physics

  • Abigail S. Gertner
  • Kurt VanLehn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1839)


Andes is an Intelligent Tutoring System for introductory college physics. The fundamental principles underlying the design of Andes are: (1) encourage the student to construct new knowledge by providing hints that require them to derive most of the solution on their own, (2) facilitate transfer from the system by making the interface as much like a piece of paper as possible, (3) give immediate feedback after each action to maximize the opportunities for learning and minimize the amount of time spent going down wrong paths, and (4) give the student flexibility in the order in which actions are performed, and allow them to skip steps when appropriate. This paper gives an overview of Andes, focusing on the overall architecture and the student’s experience using the system.


Bayesian Network Action Interpreter Intelligent Tutor System Student Model Solution Graph 
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.


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  1. [1]
    P. L. Albacete and K. A. VanLehn. The conceptual helper: An intelligent tutoring system for teaching fundamental physics concepts. In Proceedings of the Fifth International Conference on Intelligent Tutoring Systems, 2000.Google Scholar
  2. [2]
    M. Chi, P. Feltovich, and R. Glaser. Categorization and representation of physics problems by experts and novices. Cognitive Science, 5:121–152, 1981.CrossRefGoogle Scholar
  3. [3]
    C. Conati, A. S. Gertner, K. VanLehn, and M. J. Druzdzel. On-line student modeling for coached problem solving using Bayesian networks. In Proceedings of UM-97, Sixth International Conference on User Modeling, pages 231–242, Sardinia, Italy, June 1997. Springer.Google Scholar
  4. [4]
    C. Conati and K. VanLehn. Teaching meta-cognitive skills: implementation and evaluation of a tutoring system to guide self-explanation while learning from examples. In In ¿ Proceedings of AIED 99, 9th World Conference of Artificial Intelligence and Education, Le Man, France, 1999.Google Scholar
  5. [5]
    C. Conati and K. A. VanLehn. Further results from the evaluation of an intelligent computer tutor to coach self-explanation. In Proceedings of the Fifth International Conference on Intelligent Tutoring Systems, 2000.Google Scholar
  6. [6]
    A. S. Gertner. Providing feedback to equation entries in an intelligent tutoring system for physics. In Proceedings of the 4th International Conference on Intelligent Tutoring Systems, San Antonio, August 1998.Google Scholar
  7. [7]
    A. S. Gertner, C. Conati, and K. VanLehn. Procedural help in Andes: Generating hints using a Bayesian network student model. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, Madison, WI, 1998.Google Scholar
  8. [8]
    J. I. Heller and F. Reif. Prescribing effective human problem-solving processes: Problem descriptions in physics. Cognition and Instruction, 1(2):177–216, 1984.CrossRefGoogle Scholar
  9. [9]
    K. R. Koedinger, J. R. Anderson, W. H. Hadley, and M. A. Mark. Intelligent tutoring goes to school in the big city. In J. Greer, editor, Proceedings of the 7th World Conference on Artificial Intelligence and Education, pages 421–428, Charlottesville, NC, 1995.Google Scholar
  10. [10]
    A. Van Heuvelen. Learning to think like a physicist: A review of research-based instructional strategies. American Journal of Physics, 59(10):891–897, 1991.CrossRefGoogle Scholar
  11. [11]
    A. Van Heuvelen. Overview, case study physics. American Journal of Physics, 59(10):898–907, 1991.CrossRefGoogle Scholar
  12. [12]
    K. VanLehn. Conceptual and meta learning during coached problem solving. In C. Frasson, G. Gauthier, and A. Lesgold, editors, Proceedings of the Third International Conference on Intelligent Tutoring Systems ITS’96, pages 29–47. Springer, 1996.Google Scholar
  13. [13]
    K. VanLehn, R. Freedman, P. Jordan, R. C. Murray, R. Osan, M. Ringenberg, C. Rosé, K. Schulze, R. Shelby, D. Treacy, A. Weinstein, and M. Wintersgill. Fading and deepening: The next steps for andes and other model-tracing tutors. In Proceedings of the Fifth International Conference on Intelligent Tutoring Systems, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Abigail S. Gertner
    • 1
  • Kurt VanLehn
    • 2
  1. 1.The MITRE CorporationBedford
  2. 2.LRDCUniversity of PittsburghPittsburgh

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