Instructional Science

, Volume 39, Issue 5, pp 645–666

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

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

DOI: 10.1007/s11251-010-9151-4

Cite this article as:
Nokes, T.J., Hausmann, R.G.M., VanLehn, K. et al. Instr Sci (2011) 39: 645. doi:10.1007/s11251-010-9151-4

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 

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