Presentation on theme: "Intelligent Tutoring System based on Belief networks Maomi Ueno Nagaoka University of Technology."— Presentation transcript:
Intelligent Tutoring System based on Belief networks Maomi Ueno Nagaoka University of Technology
Advantages of ITS in probabilistic approaches Mathematical analysis of the system behaviors. Mathematical approximation for convenient calculation
Decision making approaches to describe a teachers behavior Assumption A Teacher behaves to maximizes the following expected utility. Expected Utility = ΣUtility×Probability
Probability model to describe human behaviors Tversky A. and Kahneman,D. 3 Tversky A. and Kahneman,D. 4 Tversky A. and Kahneman,D. 83 Simon H.A. and etc. It is impossible to describe human behaviors by using Probabilistic approaches.
Rationality Human is Rational.(probabilistic approaches) vs. Human is not Rational.(has pointed out, and seems right.)
Purposes of this study What is the utility of a teachers behavior? This paper tries to describe a teachers behavior as a simple function.
Relational works Reye, J. (1986)A belief net backbone for student modeling, Proc of Intelligent Tutoring System, pp.274- 283. R. Charles Murray and Kurt VanLehn (2000) DD Tutor:A decision-Theoretic, Dynamic approach for Optimal Selection of Tutorial Actions, Proc of Intelligent Tutoring System, pp. 153-162.
Unique features of this paper A simple utility function: Changes of the predictive student model Teachers Prior knowledge An exact parameterization of Bayesian student modeling : Predictive distribution of Bayesian networks.
Teachers actions Instruction corresponding to the js node. Ask a question corresponding to the js node.
Select the action to maximize utility function Expected Value of Instruction Information (EVII)
Stopping rule EVII < 0.0001 Probability propagation Given Instruction frame j P(x j ) p(x j =1 | x 1 =1, x 2 =1, x j-1 =1)=1 Given question frame j P(x j ) x j =1 :right answer 0:wrong answer
Examples Data: 248 Junior high school students test data
Prior parameter n 1 <n 0 P(root) = 0.0 When a teacher know that the student knowledge is poor
Strategy Bottom Up strategy (from the easy material to the difficult material) Instruction frames
Prior parameter n 1 >n 0 P(top)=1 When a teacher know that the student knowledge is excellent, Top down strategy The system presents the difficult question. If the student provides wrong answer, then the system presents more easy question and instruction.
Prior parameter n 1 =n 0 When a teacher have no knowledge about the student knowledge Flexible strategies