Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
More Accurate Student Modeling Through Contextual Estimation of Slip and Guess Probabilities in Bayesian
 

Summary: More Accurate Student Modeling Through Contextual
Estimation of Slip and Guess Probabilities in Bayesian
Knowledge Tracing
Ryan S.J.d. Baker, Albert T. Corbett, Vincent Aleven
Human-Computer Interaction Institute, Carnegie Mellon University
{rsbaker, corbett, aleven} @cmu.edu
Abstract. Modeling students' knowledge is a fundamental part of intelligent tutoring systems.
One of the most popular methods for estimating students' knowledge is Corbett and Anderson's
[6] Bayesian Knowledge Tracing model. The model uses four parameters per skill, fit using
student performance data, to relate performance to learning. Beck [1] showed that existing
methods for determining these parameters are prone to the Identifiability Problem: the same
performance data can be fit equally well by different parameters, with different implications on
system behavior. Beck offered a solution based on Dirichlet Priors [1], but, we show this solu-
tion is vulnerable to a different problem, Model Degeneracy, where parameter values violate
the model's conceptual meaning (such as a student being more likely to get a correct answer if
he/she does not know a skill than if he/she does). We offer a new method for instantiating
Bayesian Knowledge Tracing, using machine learning to make contextual estimations of the
probability that a student has guessed or slipped. This method is no more prone to problems
with Identifiability than Beck's solution, has less Model Degeneracy than competing approach-
es, and fits student performance data better than prior methods. Thus, it allows for more accu-

  

Source: Aleven, Vincent - Human Computer Interaction Institute, School of Computer Science, Carnegie Mellon University

 

Collections: Computer Technologies and Information Sciences