Using Contextual Factors Analysis to Explain Transfer of Least Common Multiple Skills

  • Philip I. PavlikJr
  • Michael Yudelson
  • Kenneth R. Koedinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6738)


Transfer of learning to new or different contexts has always been a chief concern of education because unlike training for a specific job, education must establish skills without knowing exactly how those skills might be called upon. Research on transfer can be difficult, because it is often superficially unclear why transfer occurs or, more frequently, does not, in a particular paradigm. While initial results with Learning Factors Transfer (LiFT) analysis (a search procedure using Performance Factors Analysis, PFA) show that more predictive models can be built by paying attention to these transfer factors [1, 2], like proceeding models such as AFM (Additive Factors Model) [3], these models rely on a Q-matrix analysis that treats skills as discrete units at transfer. Because of this discrete treatment, the models are more parsimonious, but may lose resolution on aspects of component transfer. To improve understanding of this transfer, we develop new logistic regression model variants that predict learning differences as a function of the context of learning. One advantage of these models is that they allow us to disentangle learning of transferable knowledge from the actual transfer performance episodes.


computational models of learning educational data mining transfer appropriate processing 


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  1. 1.
    Pavlik Jr., P.I., Cen, H., Koedinger, K.R.: Performance Factors Analysis – a New Alternative to Knowledge Tracing. In: Dimitrova, V., Mizoguchi, R. (eds.) Proceedings of the 14th International Conference on Artificial Intelligence in Education, Brighton, England (2009)Google Scholar
  2. 2.
    Pavlik Jr., P.I., Cen, H., Koedinger, K.R.: Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models. In: Barnes, T., Desmarais, M., Romero, C., Ventura, S. (eds.) Proceedings of the the 2nd International Conference on Educational Data Mining, Cordoba, Spain, pp. 121–130 (2009)Google Scholar
  3. 3.
    Cen, H., Koedinger, K.R., Junker, B.: Learning Factors Analysis – A General Method for Cognitive Model Evaluation and Improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164–175. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Thorndike, E.L., Woodworth, R.S.: The Influence of Improvement in One Mental Function Upon the Efficiency of Other Functions (I). Psychological Review 8, 247–261 (1901)CrossRefGoogle Scholar
  5. 5.
    Judd, C.H.: Special Training and General Intelligence. Education Review 36, 28–42 (1908)Google Scholar
  6. 6.
    Wertheimer, M.: Productive Thinking (1945)Google Scholar
  7. 7.
    Koedinger, K., McLaren, B.: Developing a Pedagogical Domain Theory of Early Algebra Problem Solving. CMU-HCII Tech. Report 02-100 (2002)Google Scholar
  8. 8.
    Kieras, D.E., Meyer, D.E.: The Role of Cognitive Task Analysis in the Application of Predictive Models of Human Performance. In: Schraagen, J.M., Chipman, S.F., Shalin, V.L. (eds.) Cognitive Task Analysis. Lawrence Erlbaum Associates Publishers, Mahwah (2000)Google Scholar
  9. 9.
    Barnes, T., Stamper, J., Madhyastha, T.: Comparative Analysis of Concept Derivation Using the Q-Matrix Method and Facets (2006)Google Scholar
  10. 10.
    Barnes, T.: The Q-Matrix Method: Mining Student Response Data for Knowledge. In: American Association for Artificial Intelligence 2005 Educational Data Mining Workshop (2005)Google Scholar
  11. 11.
    Simon, H.A.: The Functional Equivalence of Problem Solving Skills. Cognitive Psychology 7, 268–288 (1975)CrossRefGoogle Scholar
  12. 12.
    Pardos, Z., Heffernan, N.: Detecting the Learning Value of Items in a Randomized Problem Set. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education. IOS Press, Brighton (2009)Google Scholar
  13. 13.
    Pardos, Z., Heffernan, N.: Determining the Significance of Item Order in Randomized Problem Sets. In: Proceedings of the 2nd International Conference on Educational Data Mining, Cordoba, Spain, pp. 111–120 (2009)Google Scholar
  14. 14.
    Morris, C.D., Bransford, J.D., Franks, J.J.: Levels of Processing Versus Transfer Appropriate Processing. Journal of Verbal Learning and Verbal Behavior 16, 519–533 (1977)CrossRefGoogle Scholar
  15. 15.
    Pennington, N., Nicolich, R., Rahm, J.: Transfer of Training between Cognitive Subskills: Is Knowledge Use Specific? Cognitive Psychology 28, 175–224 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Philip I. PavlikJr
    • 1
  • Michael Yudelson
    • 1
  • Kenneth R. Koedinger
    • 1
  1. 1.Human Computer Interaction InstituteCarnegie Mellon UniversityUSA

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