On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks

This paper describes the student modeling component of ANDES, an Intelligent Tutoring System for Newtonian physics. ANDES' student model uses a Bayesian network to do long-term knowledge assessment, plan recognition and prediction of students' actions during problem solving. The network is updated in real time, using an approximate anytime algorithm based on stochastic sampling, as a student solves problems with ANDES. The information in the student model is used by ANDES' Help system to tailor its support when the student reaches impasses in the problem solving process. In this paper, we describe the knowledge structures represented in the student model and discuss the implementation of the Bayesian network assessor. We also present a preliminary evaluation of the time performance of stochastic sampling algorithms to update the network.

[1]  Max Henrion,et al.  Some Practical Issues in Constructing Belief Networks , 1987, UAI.

[2]  John Self.  Bypassing the intractable problem of student modelling , 1988 .

[3]  Sandra Carberry,et al.  Incorporating Default Inferences Into Plan Recognition , 1990, AAAI.

[4]  Elizabeth T. Whitaker,et al.  Plan recognition in intelligent tutoring systems , 1990, Intell. Tutoring Media.

[5]  Stig K. Andersen,et al.  Probabilistic reasoning in intelligent systems: Networks of plausible inference , 1991 .

[6]  Kurt VanLehn,et al.  A model of the self-explanation effect. , 1992 .

[7]  Mark E. Frisse,et al.  A tutorial introduction to stochastic simulation algorithms for belief networks , 1993, Artif. Intell. Medicine.

[8]  Jim E. Greer,et al.  Recognizing plans in instructional systems using granularity , 1993 .

[9]  Robert P. Goldman,et al.  A Bayesian Model of Plan Recognition , 1993, Artif. Intell..

[10]  大西 仁,et al.  Pearl, J. (1988, second printing 1991). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan-Kaufmann. , 1994 .

[11]  Robert J. Mislevy,et al.  Diagnostic Assessment of Troubleshooting Skill in an Intelligent Tutoring System. , 1994 .

[12]  Marek J Druzdzel,et al.  Relevance in Probabilistic Models: "Backyards" in a "Small World" , 1994 .

[13]  Edmund H. Durfee,et al.  The Automated Mapping of Plans for Plan Recognition , 1994, AAAI.

[14]  Robert J. Mislevy,et al.  PROBABILITY‐BASED INFERENCE IN COGNITIVE DIAGNOSIS , 1994 .

[15]  S. Chipman,et al.  Cognitively diagnostic assessment , 1995 .

[16]  Michael P. Wellman,et al.  Accounting for Context in Plan Recognition, with Application to Traffic Monitoring , 1995, UAI.

[17]  John R. Anderson,et al.  Cognitive Tutors: Lessons Learned , 1995 .

[18]  Padraic Monaghan,et al.  Proceedings of the 23rd annual conference of the cognitive science society , 2001 .

[19]  Kurt VanLehn,et al.  Conceptual and Meta Learning During Coached Problem Solving , 1996, Intelligent Tutoring Systems.

[20]  Jim E. Greer,et al.  Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets , 1996, Intelligent Tutoring Systems.

[21]  A. Gupta,et al.  A Bayesian Approach to , 1997 .

[22]  Yan Lin,et al.  Computational Advantages of Relevance Reasoning in Bayesian Belief Networks , 1997, UAI.