Skip to main content
Log in

The expertise reversal effect and worked examples in tutored problem solving

  • Published:
Instructional Science Aims and scope Submit manuscript

Abstract

Prior research has shown that tutored problem solving with intelligent software tutors is an effective instructional method, and that worked examples are an effective complement to this kind of tutored problem solving. The work on the expertise reversal effect suggests that it is desirable to tailor the fading of worked examples to individual students’ growing expertise levels. One lab and one classroom experiment were conducted to investigate whether adaptively fading worked examples in a tutored problem-solving environment can lead to higher learning gains. Both studies compared a standard Cognitive Tutor with two example-enhanced versions, in which the fading of worked examples occurred either in a fixed manner or in a manner adaptive to individual students’ understanding of the examples. Both experiments provide evidence of improved learning results from adaptive fading over fixed fading over problem solving. We discuss how to further optimize the fading procedure matching each individual student’s changing knowledge level.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Aleven, V., & Koedinger, K. R. (2002). An effective meta-cognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26, 147–179.

    Article  Google Scholar 

  • Aleven, V., McLaren, B. M., Sewall, J., & Koedinger, K. R. (in press). Example-tracing tutors: A new paradigm for intelligent tutoring systems. International Journal of Artificial Intelligence in Education.

  • Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4, 167–207.

    Article  Google Scholar 

  • Atkinson, R. K., Renkl, A., & Merrill, M. M. (2003). Transitioning from studying examples to solving problems: Combining fading with prompting fosters learning. Journal of Educational Psychology, 95, 774–783.

    Article  Google Scholar 

  • Baker, R. S. J. d., Corbett, A. T., & Aleven, V. (2008). More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the 9th international conference on intelligent tutoring systems (pp. 406–415). Berlin: Springer Verlag.

  • Cen, H., Koedinger, K. R., & Junker, B. (2007). Is over practice necessary?—improving learning efficiency with the Cognitive Tutor through educational data mining. In R. Luckin, K. R. Koedinger, & J. Greer (Eds.), Proceedings of 13th international conference on artificial intelligence in education (AIED2007) (pp. 511–518). Amsterdam: IOS Press.

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York: Academic Press.

    Google Scholar 

  • Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 253–278.

    Article  Google Scholar 

  • Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-entailed instruction. Educational Psychology Review, 19, 509–539.

    Article  Google Scholar 

  • Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.

    Article  Google Scholar 

  • Kalyuga, S., & Sweller, J. (2005). Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology Research and Development, 53, 83–93.

    Article  Google Scholar 

  • Koedinger, K. R., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with Cognitive Tutors. Educational Psychology Review, 19, 239–264.

    Article  Google Scholar 

  • 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 

  • Koedinger, K. R., Corbett, A. T., Ritter, S., & Shapiro, L. (2000). Carnegie learning’s cognitive tutor™: Summary research results. (Available from Carnegie Learning, Inc., Pittsburgh, PA): http://www.carnegielearning.com.

  • McLaren, B. M., Lim, S., Yaron, D., & Koedinger, K. R. (2007). Can a polite intelligent tutoring system lead to improved learning outside of the lab? In R. Luckin, K. R. Koedinger, & J. Greer (Eds), Proceedings of the 13th international conference on artificial intelligence in education (AIED-07), Artificial Intelligence in Education: Building technology rich learning contexts that work (pp. 433–440). IOS Press.

  • Renkl, A., & Atkinson, R. K. (2007). An example order for cognitive skill acquisition. In F. E. Ritter, J. Nerb, E. Lehtinen, & T. O’Shea (Eds.), In order to learn: How the sequence of topics influences learning (pp. 95–105). New York, NY: Oxford University Press.

    Google Scholar 

  • Renkl, A., Atkinson, R. K., & Große, C. S. (2004). How fading worked solution steps works—a cognitive load perspective. Instructional Science, 32, 59–82.

    Article  Google Scholar 

  • Renkl, A., Atkinson, R. K., Maier, U. H., & Staley, R. (2002). From example study to problem solving: Smooth transitions help learning. Journal of Experimental Education, 70, 293–315.

    Article  Google Scholar 

  • Roy, M., & Chi, M. T. H. (2005). Self-explanation in a multi-media context. In R. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 271–286). Cambridge: Cambridge Press.

    Google Scholar 

  • Schwonke, R., Wittwer, J., Aleven, V., Salden, R. J. C. M., Krieg, C., & Renkl, A. (2007). Can tutored problem solving benefit from faded worked-out examples? In S. Vosniadou, D. Kayser, & A. Protopapas (Eds.), Proceedings of EuroCogSci 07. The european cognitive science conference 2007 (pp. 59–64). New York, NY: Erlbaum.

  • Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59–89.

    Article  Google Scholar 

  • Van Lehn, K., Lynch, C., Schulze, K., Shapiro, J. A., Shelby, A., Taylor, D., Weinstein, A., & Wintersgill, M. (2005). The Andes physics tutoring project: Five years of evaluations. International Journal of Artificial Intelligence in Education, 15, 1–47.

    Google Scholar 

  • Ward, M., & Sweller, J. (1990). Structuring effective worked examples. Cognition and Instruction, 7, 1–39.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Pittsburgh Science of Learning Center which is funded by the National Science Foundation award number SBE-0354420.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ron J. C. M. Salden.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Salden, R.J.C.M., Aleven, V., Schwonke, R. et al. The expertise reversal effect and worked examples in tutored problem solving. Instr Sci 38, 289–307 (2010). https://doi.org/10.1007/s11251-009-9107-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11251-009-9107-8

Keywords

Navigation