Intelligent Tutoring System based on Belief networks
Upcoming SlideShare
Loading in...5
×
 

Intelligent Tutoring System based on Belief networks

on

  • 822 views

Maomi Ueno

Maomi Ueno
International Workshop on Advanced Learning Technologies, 2000
Pages: 141-141 , DOI: 10.1109/IWALT.2000.890590
ICALT

Statistics

Views

Total Views
822
Views on SlideShare
822
Embed Views
0

Actions

Likes
0
Downloads
3
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Intelligent Tutoring System based on Belief networks Intelligent Tutoring System based on Belief networks Presentation Transcript

  • Intelligent TutoringSystem 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 teacher’s behaviorAssumption A Teacher behaves to maximizes the following expected utility.Expected Utility = ΣUtility×Probability View slide
  • Probability model to describe human behaviors Tversky 、 A.   and Kahneman,D. 197 3 Tversky 、 A.   and Kahneman,D. 197 4 Tversky 、 A.   and Kahneman,D. 19 83 Simon , H.A.  1974 and etc. ↓It is impossible to describe human behaviors by using Probabilistic approaches. View slide
  • 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 teacher’s behavior? This paper tries to describe a teacher’s 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 Teacher’s Prior knowledge An exact parameterization of Bayesian student modeling : Predictive distribution of Bayesian networks.
  • Student model  Bayesian Belief networks 15 appli cati on problem 14 ax + b =cx + d  type{x1 .⊆2{,,x 2q,i  , x qi }Π i , x , x1 x }xi 13 ax + bx = c type N 12 ax + b= c type P ( X 1 , X 2 , , X N | S ) = ∏ p ( xi | Π i , S ) ax = b type i =1 11 10 x + a = b type substi tui on representati on of di vi si on subtracti on 8 9 equati on 5 3 7 li teral representati on 4 2 multi pli cati on addi ti on 1 6 posi ti ve and negati ve number Fi gi ure 1. An example of the student model
  • Prior distribution as a Prior Knowledge Dirichret distribution ,which is a conjecture distribution of the Bayesian networks 1 N qI Γ(∑n ijk ) 1 −1 p (ΘS | S ) =  1 k =0  θijk n IJK i=1 j=1  Γ( n ijk ) k =0 k =0
  • Predictive distribution as a student model qi N Γ(n ijk )  Γ(n ijk +nijk )p( X | S ) = ∏ j i =1 [Γ(n ijk )] Γ( n ijk +n ijk )
  • Teacher’s actions Instruction corresponding to the j’s node. Ask a question corresponding to the j’s node.
  • Select the action to maximize utility functionExpected Value of Instruction Information(EVII) n ∑2 qi i−1EVII = ∑ p( x , x l =1 1 n | actioni ) log p ( x1 ,  xn | actioni ) n ∑2 qi i−1− ∑ p( x , x l =1 1 n ) log p ( x1 ,  xn )
  • Probability propagationGiven Instruction frame jP(xj) →p(xj=1 | x1=1, x2=1, xj-1=1)=1Given question frame jP(xj) → xj =1 :right answer 0:wrong answer Stopping rule EVII < 0.0001
  • Examples Data: 248 Junior high school students test data 15 appli cati on problem 14 ax + b =cx + d  type 13 ax + bx = c type 12 ax + b= c type ax = b type 11 10 x + a = b type substi tui on representati on of di vi si on subtracti on 8 9 equati on 5 3 7 li teral representati on 4 2 multi pli cati on addi ti on 1 6 posi ti ve and negati ve number Fi gi ure 1. An example of the student model
  • Prior parameter n1’ <n0’ 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 n1’ >n0’ P(top)=1 When a teacher know that the student knowledge is excellent,Top down strategyThe system presents the difficult question. If the student provides wrong answer, then the system presents more easy question and instruction.
  • Prior parameter n1’ =n0’ When a teacher have no knowledge about the student knowledge Flexible strategies
  • 0 .0 0 .0 0 .1 0 .1 0 .1 0 .0 0 .1 0 .1 0 .2 0 .0 0 .1 0 .1 0 .4 0 .0 0 .0 0 .0 0 .4 0 .3 0 .3 0 .4 0 .6 0 .5 0 .5 0 .6 0 .9 0 .8 0 .7 0 .9 0 .9 0 .6 1.0 0 .9 1.0 0 .9 0 .6 0 .8 0 .8 0 .7 0 .7 0 .9 0. 0. 0. 0. 0 .90 .9 0 .9 8 0 .90 .9 0 .8 7 0 .9 .9 0 0 .90 .9 0 .8 7 1 9 0 .9 0 .8 0 .9 1 Quesi on Questi on Questi on Questi on Frame 15 Fra me 12 Fra me 9 Frame 3 0 .1 0 .1 0 .7 0 .8 0 .1 0 .4 0 .8 1.0 0 .1 0 .6 1.0 1.0 0 .0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0 .9 0 .9 0 .9 0 .91.0 0 .9 0 .9 1.0 0 .9 0 .9 1.0 0 .9 0 .9 1.0 0 .9 0 .9 0 .9 0 .9 0 .9 0 .9 0. 0. 0. 0. 0 .9 .9 0 1 0 .9 .9 0 1 9 0 .9 .9 0 1 9 0 .9 .9 0 1 9 9 1 Instructi on Instructi on Questi on Instructi on Fra me 11 Frame 12 Fra me 13 Fra me 14 1.0 1.0 1.0 1.0 1.0 0 .9 1.0 0 .9 0 .9 0 .9 0. 0 .9 .9 0 1 9 Instructi on Fra me 15
  • Strategy Diagnose the student knowledge states Then, the system instructs knowledge which student can not understand by using the bottom-up strategy.
  • Conclusions The prior knowledge for the student Prediction of student’s knowledge A simple utility function: How can the teacher change the student’s predicted knowledge states.
  • Future tasks We are developing large scale ITS based on this study. How can we evaluate the behaviors of the system? Good or bad?