Instructional Science

, Volume 39, Issue 5, pp 645–666 | Cite as

Testing the instructional fit hypothesis: the case of self-explanation prompts

  • Timothy J. Nokes
  • Robert G. M. Hausmann
  • Kurt VanLehn
  • Sophia Gershman
Article

Abstract

Cognitive science principles should have implications for the design of effective learning environments. The self-explanation principle was chosen for the current work because it has developed significantly over the last 20 years. Early formulations hypothesized that self-explanation facilitated inference generation to supply missing information about a concept or target skill, whereas later work hypothesized that self-explanation facilitated mental-model revision (Chi, Handbook of research on conceptual change, 2000). To better understand the complex relationship between prior knowledge, cognitive processing, and changes to a learner’s representation, two classes of self-explanation prompts (gap-filling and mental-model revision) were tested in the domain of physics problem solving. Prompts designed to focus the learner on gap-filling led to greater learning and a reduction in the amount of tutoring assistance required to solve physics problems. The results are interpreted as support for the instructionalfit hypothesis—the idea that the efficacy of instruction is contingent on the match between the cognitive processing that the instruction elicits, how those processes modify the underlying knowledge representations for the task, and the utility of those representations for the task or problem.

Keywords

Self-explanation Prompting Worked examples Intelligent tutoring systems 

References

  1. Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26, 147–179.CrossRefGoogle Scholar
  2. Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  3. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207.CrossRefGoogle Scholar
  4. Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  5. Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004). Off-task behavior in the Cognitive Tutor classroom: When students “game the system”. Proceedings of ACM CHI 2004: Computer-Human Interaction, 383–390.Google Scholar
  6. Berthold, K., Eysink, T. H., & Renkl, A. (2009). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 37, 345–363.CrossRefGoogle Scholar
  7. Catrambone, R. (1998). The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General, 127(4), 355–376.CrossRefGoogle Scholar
  8. Chapin, S., O’Connor, C., & Anderson, N. (2003). Classroom discussions: Using math talk to help students learn, grades 1–6. Sausalito, CA: Math Solutions Publications.Google Scholar
  9. Chi, M. T. H. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology (pp. 161–238). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.Google Scholar
  10. Chi, M. T. H. (2008). Three kinds of conceptual change: Belief revision, mental model transformation, and ontological shift. In S. Vosniadou (Ed.), Handbook of research on conceptual change (pp. 61–82). New York, NY: Routledge.Google Scholar
  11. Chi, M. T. H., & Bassok, M. (1989). Learning from examples via self-explanations. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 251–282). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.Google Scholar
  12. Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.Google Scholar
  13. Cohen, J. (1988). Statistical power analysis of the behavioral sciences (2nd ed.). New York: Academic Press.Google Scholar
  14. Conati, C., & VanLehn, K. (2000). Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education, 11, 398–415.Google Scholar
  15. Hausmann, R. G. M., & Chi, M. T. H. (2002). Can a computer interface support self-explaining? Cognitive Technology, 7(1), 4–14.Google Scholar
  16. Hausmann, R. G. M., & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger, & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417–424). Amsterdam: IOS Press.Google Scholar
  17. Keppel, G. (1991). Design and analysis: A researcher’s guide. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  18. Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30–43.Google Scholar
  19. Maloney, D. P., O’Kuma, T. L., Hieggelke, C. J., & Van Heuvelen, A. (2001). Surveying students’ conceptual knowledge of electricity and magnetism. American Journal of Physics, 69(7), S12–S23.CrossRefGoogle Scholar
  20. Marshall, S. P. (1995). Schemas in problem solving. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  21. McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38(1), 1–30.CrossRefGoogle Scholar
  22. Nokes, T. J., Schunn, C. D., & Chi, M. T. H. (2010). Problem solving and human expertise. In P. Peterson, E. Baker, & B. McGraw (Eds.), International encyclopedia of education (Vol. 5, pp. 265–272). Oxford: Elsevier.CrossRefGoogle Scholar
  23. Olejnik, S., & Algina, J. (2000). Measures for effect size for comparative studies: Applications, interpretations, and limitations. Contemporary Educational Psychology, 25, 241–286.CrossRefGoogle Scholar
  24. Pashler, H., Bain, P., Bottge, B., Graesser, A., Koedinger, K., McDaniel, M., & Metcalfe, J. (2007). Organizing Instruction and Study to Improve Student Learning (NCER 2007–2004). Washington, DC: National Center for Education Research, Institute of Education Sciences, U.S. Department of Education.Google Scholar
  25. Renkl, A. (2002). Learning from worked-out examples: Instructional explanations supplement self-explanations. Learning & Instruction, 12, 529–556.CrossRefGoogle Scholar
  26. Rittle-Johnson, B. (2006). Promoting transfer: Effects of self-explanation and direct instruction. Child Development, 77, 1–15.CrossRefGoogle Scholar
  27. Ross, B. H. (1984). Remindings and their effects in learning a cognitive skill. Cognitive Psychology, 16, 371–416.CrossRefGoogle Scholar
  28. Ross, B. H., & Kilbane, M. C. (1997). Effects of principle explanation and superficial similarity on analogical mapping in problem solving. Journal of Experimental Psychology. Learning, Memory, and Cognition, 23(2), 427–440.CrossRefGoogle Scholar
  29. Singley, M. K., & Anderson, J. R. (1989). The transfer of cognitive skill. Cambridge, MA: Harvard University Press.Google Scholar
  30. VanLehn, K. (1998). Analogy events: How examples are used during problem solving. Cognitive Science, 22(3), 347–388.CrossRefGoogle Scholar
  31. VanLehn, K., Lynch, C., Schultz, K., Shapiro, J. A., Shelby, R., Taylor, L., et al. (2005). The Andes physics tutoring system: Lessons learned. International Journal of Artificial Intelligence and Education, 15(3), 147–204.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Timothy J. Nokes
    • 1
  • Robert G. M. Hausmann
    • 2
  • Kurt VanLehn
    • 3
  • Sophia Gershman
    • 4
  1. 1.Department of Psychology, Learning Research and Development CenterUniversity of PittsburghPittsburghUSA
  2. 2.Carnegie Learning, Inc.PittsburghUSA
  3. 3.School of Computing, Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA
  4. 4.Watchung Hills Regional High SchoolWarrenUSA

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