User Modeling and UserAdapted Interaction
, Volume 12, Issue 4, pp 371417
Using Bayesian Networks to Manage Uncertainty in Student Modeling
 Cristina ConatiAffiliated withDepartment of Computer Science, University of British Columbia
 , Abigail GertnerAffiliated withThe MITRE Corporation
 , Kurt VanLehnAffiliated withDepartment of Computer Science and Learning and Research Development Center, University of Pittsburgh
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When a tutoring system aims to provide students with interactive help, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. That is, it must do both assessment and plan recognition. These modeling tasks involve a high level of uncertainty when students are allowed to follow various lines of reasoning and are not required to show all their reasoning explicitly. We use Bayesian networks as a comprehensive, sound formalism to handle this uncertainty. Using Bayesian networks, we have devised the probabilistic student models for Andes, a tutoring system for Newtonian physics whose philosophy is to maximize student initiative and freedom during the pedagogical interaction. Andes’ models provide longterm knowledge assessment, plan recognition, and prediction of students’ actions during problem solving, as well as assessment of students’ knowledge and understanding as students read and explain worked out examples. In this paper, we describe the basic mechanisms that allow Andes’ student models to soundly perform assessment and plan recognition, as well as the Bayesian network solutions to issues that arose in scaling up the model to a fullscale, field evaluated application. We also summarize the results of several evaluations of Andes which provide evidence on the accuracy of its student models.
 Title
 Using Bayesian Networks to Manage Uncertainty in Student Modeling
 Journal

User Modeling and UserAdapted Interaction
Volume 12, Issue 4 , pp 371417
 Cover Date
 200211
 DOI
 10.1023/A:1021258506583
 Print ISSN
 09241868
 Online ISSN
 15731391
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 student modelling
 Intelligent Tutoring Systems
 dynamic Bayesian networks
 Industry Sectors
 Authors

 Cristina Conati ^{(1)}
 Abigail Gertner ^{(2)}
 Kurt VanLehn ^{(3)}
 Author Affiliations

 1. Department of Computer Science, University of British Columbia, Vancouver, BC, V6T1Z4, Canada
 2. The MITRE Corporation, 202 Burlington Road, Bedford, MA, 01730, USA
 3. Department of Computer Science and Learning and Research Development Center, University of Pittsburgh, Pittsburgh, PA, 15260, USA