Scaffolding Problem Solving with Annotated, Worked-Out Examples to Promote Deep Learning

  • Michael A. Ringenberg
  • Kurt VanLehn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)


This study compares the relative utility of an intelligent tutoring system that uses procedure-based hints to a version that uses worked-out examples for learning college level physics. In order to test which strategy produced better gains in competence, two versions of Andes were used: one offered participants graded hints and the other offered annotated, worked-out examples in response to their help requests. We found that providing examples was at least as effective as the hint sequences and was more efficient in terms of the number of problems it took to obtain the same level of mastery.


Grade Point Average Deep Structure Training Problem Homework Problem Cumulative Grade Point Average 


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  1. 1.
    Bloom, B.S.: The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher 13, 4–16 (1984)Google Scholar
  2. 2.
    Sweller, J., Cooper, G.A.: The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction 2(1), 59–89 (1985)CrossRefGoogle Scholar
  3. 3.
    Cooper, G., Sweller, J.: Effects of schema acquisition and rule automation on mathematical problem-solving transfer. Journal of Educational Psychology 79(4), 347–362 (1987)CrossRefGoogle Scholar
  4. 4.
    Brown, D.E.: Using examples and analogies to remediate misconceptions in physics: Factors influencing conceptual change. Journal of Research in Science Teaching 29(1), 17–34 (1992)CrossRefGoogle Scholar
  5. 5.
    Catrambone, R.: Aiding subgoal learning - effects on transfer. Journal of Educational Psychology 87(1), 5–17 (1995)CrossRefGoogle Scholar
  6. 6.
    Koehler, M.J.: Designing case-based hypermedia for developing understanding children’s mathematical reasoning. Cognition and Instruction 20(2), 151–195 (2002)CrossRefMathSciNetGoogle Scholar
  7. 7.
    VanLehn, K., Lynch, C., Schulze, K., Shapiro, J.A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., Wintersgill, M.: Andes physics tutoring system: Five years of evaluations. In: Looi, G.M.C.K. (ed.) Proceedings of the 12th International Conference on Artificial Intelligence in Education. IOS Press, Amsterdam (2005)Google Scholar
  8. 8.
    Aleven, V., Koedinger, K.R.: Investigations into help seeking and learning with a cognitive tutor. In: Papers of the AIED 2001 Workshop on Help Provision and Help Seeking in Interactive Learning Environments, pp. 47–58 (2001)Google Scholar
  9. 9.
    LeFevre, J.A., Dixon, P.: Do written instructions need examples? Cognition and Instruction 3(1), 1–30 (1986)CrossRefGoogle Scholar
  10. 10.
    Trafton, J.G., Reiser, B.J.: The contribution of studying examples and solving problems to skill acquisition. In: Proceedings of the 15th Annual Conference of the Cognitive Science Society, pp. 1017–1022. Lawrence Erlbaum Associates, Inc., Hillsdale (1993)Google Scholar
  11. 11.
    Chi, M.T.H., Bassok, M., Lewis, M.W., Reimann, P., Glaser, R.: Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science 13(2), 145–182 (1989)CrossRefGoogle Scholar
  12. 12.
    Chi, M.T.H., VanLehn, K.A.: The content of physics self-explanations. Journal of the Learning Sciences 1(1), 69–105 (1991)CrossRefGoogle Scholar
  13. 13.
    VanLehn, K., Johns, R.M.: Better learners use analogical problem solving sparingly. In: Utgoff, P.E. (ed.) Machine Learning: Proceedings of the Tenth Annual Conference, pp. 338–345. Morgan Kaufmann, San Mateo (1993)Google Scholar
  14. 14.
    Dufresne, R.J., Gerace, W.J., Hardiman, P.T., Mestre, J.P.: Constraining novices to perform expertlike problem analyses: Effects on schema acquisition. Journal of the Learning Sciences 2(3), 307–331 (1992)CrossRefGoogle Scholar
  15. 15.
    Kalyuga, S., Ayres, P., Chandler, P., Sweller, J.: The expertise reversal effect. Educational Psychologist 38(1), 23–31 (2003)CrossRefGoogle Scholar
  16. 16.
    Murray, R.C., VanLehn, K.: DT Tutor: A Decision-Theoretic, Dynamic Approach for Optimal Selection of Tutorial Actions. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 153–162. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael A. Ringenberg
    • 1
  • Kurt VanLehn
    • 1
  1. 1.Learning Research and Development CenterUniversity of PittsburghPittsburghUSA

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