Procedural help in Andes: Generating hints using a Bayesian network student model

Abigail S. Gertner, Cristina Conati, Kurt VanLehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

84 Scopus citations

Abstract

One of the most important problems for an intelligent tutoring system is deciding how to respond when a student asks for help. Responding cooperatively requires an understanding of both what solution path the student is pursuing, and the student's current level of domain knowledge. Andes, an intelligent tutoring system for Newtonian physics, refers to a probabilistic student model to make decisions about responding to help requests. Andes' student model uses a Bayesian network that computes a probabilistic assessment of three kinds of information: (1) the student's general knowledge about physics, (2) the student's specific knowledge about the current problem, and (3) the abstract plans that the student may be pursuing to solve the problem. Using this model, Andes provides feedback and hints tailored to the student's knowledge and goals.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Editors Anon
Place of PublicationMenlo Park, CA, United States
PublisherAAAI
Pages106-111
Number of pages6
StatePublished - 1998
Externally publishedYes
EventProceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI - Madison, WI, USA
Duration: Jul 26 1998Jul 30 1998

Other

OtherProceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI
CityMadison, WI, USA
Period7/26/987/30/98

ASJC Scopus subject areas

  • Software

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