Spatial Reasoning in Comparative Analyses of Physics Diagrams

  • Maria D. Chang
  • Jon W. Wetzel
  • Kenneth D. Forbus
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8684)

Abstract

Spatial reasoning plays a critical role in STEM problem solving. Physics assessments, for example, are rich in diagrams and pictures, which help people understand concrete physical scenarios and abstract aspects of physical reasoning. In this paper we describe a system that analyzes sketched diagrams to solve qualitative physics problems from a popular physics textbook. Causal models describing each problem are formulated via visual and conceptual analyses of the sketched diagrams. We use a combination of qualitative and quantitative reasoning to solve vector addition, tension, and gravitation ranking problems in the introductory chapters of the book.

Keywords

Spatial reasoning spatial problem solving qualitative reasoning conceptual physics diagrammatic reasoning 

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References

  1. 1.
    Tversky, B.: What do sketches say about thinking. In: 2002 AAAI Spring Symposium, Sketch Understanding Workshop, Stanford University, AAAI Technical Report SS-02-08, pp. 148–151 (2002)Google Scholar
  2. 2.
    Larkin, J.H., Simon, H.A.: Why a diagram is (sometimes) worth ten thousand words. Cognitive Science 11, 65–100 (1987)Google Scholar
  3. 3.
    Suwa, M., Tversky, B., Gero, J., Purcell, T.: Seeing into sketches: Regrouping parts encourages new interpretations. In: Gero, J., Tversky, B., Purcell, T. (eds.) Visual and Spatial Reasoning in Design II, University of Sydney, Sydney, pp. 207–219 (2001)Google Scholar
  4. 4.
    Ainsworth, S., Prain, V., Tytler, R.: Drawing to learn in science. Science 3, 5 (2011)Google Scholar
  5. 5.
    Wai, J., Lubinski, D., Benbow, C.P.: Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance. Journal of Educational Psychology 101, 817 (2009)Google Scholar
  6. 6.
    Chaudhri, V.K., Clark, P.E., Mishra, S., Pacheco, J., Spaulding, A.: Aura: Capturing knowledge and answering questions on science textbooks (2009), http://www.ai.sri.com/pubs/files/1768.pdf
  7. 7.
    Hestenes, D., Wells, M., Swackhamer, G.: Force concept inventory. The Physics Teacher 30, 141 (1992)Google Scholar
  8. 8.
    Hewitt, P.: Conceptual Physics. Pearson-Addison Wesley, Boston (2010)Google Scholar
  9. 9.
    Forbus, K., Usher, J., Lovett, A., Lockwood, K., Wetzel, J.: Cogsketch: Sketch understanding for cognitive science research and for education. Topics in Cognitive Science 3, 648–666 (2011)Google Scholar
  10. 10.
    Cohn, A.G., Bennett, B., Gooday, J., Gotts, N.M.: Qualitative spatial representation and reasoning with the region connection calculus. Geoinformatica 1, 275–316 (1997)Google Scholar
  11. 11.
    Lovett, A., Kandaswamy, S., McLure, M., Forbus, K.: Evaluating Qualitative Models of Shape Representation. In: 26th International Workshop on Qualitative Reasoning (2012)Google Scholar
  12. 12.
    Falkenhainer, B., Forbus, K.D., Gentner, D.: The structure-mapping engine: Algorithm and examples. Artificial intelligence 41,1–63 (1989)Google Scholar
  13. 13.
    Yin, P., Forbus, K.D., Usher, J.M., Sageman, B., Jee, B.D.: Sketch Worksheets: A Sketch-Based Educational Software System. In: Innovative Applications of Artificial Intelligence, IAAI (2010)Google Scholar
  14. 14.
    Wetzel, J., Forbus, K.: Integrating Open-Domain Sketch Understanding with Qualitative Two-Dimensional Rigid-Body Mechanics. In: Proceedings of the 22nd International Workshop on Qualitative Reasoning (2008)Google Scholar
  15. 15.
    Kim, H.: Qualitative reasoning about fluids and mechanics. Ph.D. dissertation and ILS Technical Report, Northwestern University, Evanston, IL (1993)Google Scholar
  16. 16.
    Nielson, P.E.: A qualitative approach to rigid body mechanics. Tech. Rep. No. UIUCDCS-R-88-1469; UILU-ENG-88-1775, University of Illinois at Urbana-Champaign, Department of Computer Science (1988)Google Scholar
  17. 17.
    Forbus, K.D.: Qualitative process theory. Artificial intelligence 24, 85–168 (1984)Google Scholar
  18. 18.
    Friedman, S.E., Forbus, K.D.: Repairing incorrect knowledge with model formulation and metareasoning. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 887–893 (2011)Google Scholar
  19. 19.
    Klenk, M., Forbus, K.: Analogical model formulation for transfer learning in AP Physics. Artificial intelligence 173, 1615–1638 (2009)Google Scholar
  20. 20.
    Weld, D.S.: Comparative analysis. Artificial intelligence 36, 333–373 (1988)Google Scholar
  21. 21.
    Chang, M.D., Wetzel, J., Forbus, K.D.: Qualitative and Quantitative Reasoning over Physics Textbook Diagrams. In: 25th International Workshop on Qualitative Reasoning (2011)Google Scholar
  22. 22.
    Klenk, M., Forbus, K.D., Tomai, E., Kim, H., Kyckelhahn, B.: Solving everyday physical reasoning problems by analogy using sketches. In: AAAI, p. 209 (2005)Google Scholar
  23. 23.
    Novak, G.S., Bulko, W.C.: Understanding Natural Language with Diagrams. In: AAAI, pp. 465–470 (1990)Google Scholar
  24. 24.
    Rajagopalan, R.: Picture semantics for integrating text and diagram input. Artificial intelligence Review 10, 321–344 (1996)Google Scholar
  25. 25.
    Lockwood, K., Forbus, K.: Multimodal knowledge capture from text and diagrams. In: Fifth International Conference on Knowledge Capture, pp. 65–72. ACM (2009)Google Scholar
  26. 26.
    Graesser, A.C., Chipman, P., Haynes, B.C., Olney, A.: AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions on Education 48, 612–618 (2005)CrossRefGoogle Scholar
  27. 27.
    VanLehn, K., Jordan, P.W., Penstein Rosé, C., Bhembe, D., Böttner, M., Gaydos, A., Makatchev, M., Pappuswamy, U., Ringenberg, M.A., Roque, A.C., Siler, S., Srivastava, R.: The architecture of why2-atlas: A coach for qualitative physics essay writing. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 158–167. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  28. 28.
    VanLehn, K., Lynch, C., Schulze, K., Shapiro, J.A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., Wintersgill, M.: The Andes physics tutoring system: Five years of evaluations. In: 12th International Conference on Artificial Intelligence in Education, pp. 678–685. IOS Press (2005)Google Scholar
  29. 29.
    Klenk, M., Forbus, K.: Exploiting persistent mappings in cross-domain analogical learning of physical domains. Artificial Intelligence 195, 398–417 (2013)Google Scholar
  30. 30.
    Pisan, Y.: An Integrated Architecture for Engineering Problem Solving. Doctoral Dissertation, Northwestern University, Evanston, IL. UMI No. 733042431 (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maria D. Chang
    • 1
  • Jon W. Wetzel
    • 1
  • Kenneth D. Forbus
    • 1
  1. 1.Qualitative Reasoning GroupNorthwestern UniversityEvanstonUSA

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