Abstract
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.
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References
Merrill, D.C., Reiser, B.J., Merrill, S.K., Landes, S.: Tutoring: Guided learning by doing. Cognition and Instruction 13(3), 315–372 (1995)
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)
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)
Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Francisco (1988)
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)
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)
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)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)
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)
Mayo, M., Mitrovic, A.: Optimising ITS behaviour with Bayesian networks and decision theory. International Journal of Artificial Intelligence in Education 12, 124–153 (2001)
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
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)
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)
Anderson, J.R., Lebiere, C.: The atomic components of thought. Erlbaum, NJ (1998)
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)
Fox, B.A.: The Human Tutorial Dialogue Project: Issues in the Design of Instructional Systems. Lawrence Erlbaum Associates, Hillsdale (1993)
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/
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)
Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Erlbaum, Mahwah (1988)
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)
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Murray, R.C., VanLehn, K. (2006). A Comparison of Decision-Theoretic, Fixed-Policy and Random Tutorial Action Selection. In: Ikeda, M., Ashley, K.D., Chan, TW. (eds) Intelligent Tutoring Systems. ITS 2006. Lecture Notes in Computer Science, vol 4053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11774303_12
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DOI: https://doi.org/10.1007/11774303_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35159-7
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