A Comparison of Decision-Theoretic, Fixed-Policy and Random Tutorial Action Selection

  • R. Charles Murray
  • Kurt VanLehn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)


DT Tutor (DT), an ITS that uses decision theory to select tutorial actions, was compared with both a Fixed-Policy Tutor (FT) and a Random Tutor (RT). The tutors were identical except for the method they used to select tutorial actions: FT employed a common fixed policy while RT selected randomly from relevant actions. This was the first comparison of a decision-theoretic tutor with a non-trivial competitor (FT). In a two-phase study, first DT’s probabilities were learned from a training set of student interactions with RT. Then a panel of judges rated the actions that RT took along with the actions that DT and FT would have taken in identical situations. DT was rated higher than RT and also higher than FT both overall and for all subsets of scenarios except help requests, for which DT’s and FT’s ratings were equivalent.


Action Selection Current Step Cognitive Tutor Composite Rating Null Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Merrill, D.C., Reiser, B.J., Merrill, S.K., Landes, S.: Tutoring: Guided learning by doing. Cognition and Instruction 13(3), 315–372 (1995)CrossRefGoogle Scholar
  2. 2.
    Graesser, A.C., Person, N.K., Magliano, J.P.: Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied Cognitive Psychology 9, 495–522 (1995)CrossRefGoogle Scholar
  3. 3.
    Jameson, A.: Numerical uncertainty management in user and student modeling: An overview of systems and issues. User Modeling and User-Adapted Interaction 5(3-4), 193–251 (1996)CrossRefGoogle Scholar
  4. 4.
    Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  5. 5.
    Merrill, D.C., Reiser, B.J., Ranney, M., Trafton, J.G.: Effective tutoring tech-niques: A comparison of human tutors and intelligent tutoring systems. The Journal of the Learning Sciences 2(3), 277–306 (1992)CrossRefGoogle Scholar
  6. 6.
    Lepper, M.R., Woolverton, M., Mumme, D.L., Gurtner, J.-L.: Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors. In: Lajoie, S.P., Derry, S.J. (eds.) Computers as Cognitive Tools, pp. 75–105. Erlbaum, Mahwah (1993)Google Scholar
  7. 7.
    Reye, J.: A goal-centred architecture for intelligent tutoring systems. In: Greer, J. (ed.) 7th World Conference on Artificial Intelligence in Education, pp. 307–314 (1995)Google Scholar
  8. 8.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)MATHGoogle Scholar
  9. 9.
    Murray, R.C., VanLehn, K., Mostow, J.: Looking ahead to select tutorial actions: A decision-theoretic approach. International Journal of Artificial Intelligence in Education 14(3-4), 235–278 (2004)Google Scholar
  10. 10.
    Mayo, M., Mitrovic, A.: Optimising ITS behaviour with Bayesian networks and decision theory. International Journal of Artificial Intelligence in Education 12, 124–153 (2001)Google Scholar
  11. 11.
    Pek, P.-K.: Decision-Theoretic Intelligent Tutoring System. PhD dissertation, National University of Singapore, Department of Industrial & Systems Engineering (2003), ftp://ftp.medcomp.comp.nus.edu.sg/pub/pohkl/pekpk-thesis-2003.pdf
  12. 12.
    Conati, C., Gertner, A., VanLehn, K.: Using Bayesian networks to manage uncer-tainty in student modeling. User Modeling and User-Adapted Interaction 12(4), 371–417 (2002)MATHCrossRefGoogle Scholar
  13. 13.
    Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive Tutors: Lessons Learned. The Journal of the Learning Sciences 4(2), 167–207 (1995)CrossRefGoogle Scholar
  14. 14.
    Anderson, J.R., Lebiere, C.: The atomic components of thought. Erlbaum, NJ (1998)Google Scholar
  15. 15.
    Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A.: Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education 8, 30–43 (1997)Google Scholar
  16. 16.
    Fox, B.A.: The Human Tutorial Dialogue Project: Issues in the Design of Instructional Systems. Lawrence Erlbaum Associates, Hillsdale (1993)Google Scholar
  17. 17.
    Murray, R.C.: An evaluation of decision-theoretic tutorial action selection. PhD dissertation, University of Pittsburgh, Intelligent Systems Program (2005), http://etd.library.pitt.edu/ETD/available/etd-08182005-131235/
  18. 18.
    Mostow, J., Huang, C., Tobin, B.: Pause the Video: Quick but quantitative ex-pert evaluation of tutorial choices in a Reading Tutor that listens. In: Moore, J.D., Red-field, C.L., Johnson, W.L. (eds.) 10th International Conference on Artificial Intelligence in Education, pp. 343–353 (2001)Google Scholar
  19. 19.
    Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Erlbaum, Mahwah (1988)MATHGoogle Scholar
  20. 20.
    Aleven, V., Koedinger, K.R.: Limitations of Student Control: Do Students Know When They Need Help? In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 292–303. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • R. Charles Murray
    • 1
  • Kurt VanLehn
    • 2
  1. 1.Carnegie Learning, Inc.PittsburghUSA
  2. 2.Computer Science Department & Learning Research and Development CenterUniversity of PittsburghPittsburghUSA

Personalised recommendations