Open AccessTHE ROLE OF PROBABILITY‐BASED INFERENCE IN AN INTELLIGENT TUTORING SYSTEM
Author(s) -
Mislevy Robert J.,
Gitomer Drew H.
Publication year - 1995
Publication title -
ets research report series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.235
H-Index - 5
ISSN - 2330-8516
DOI - 10.1002/j.2333-8504.1995.tb01676.x
Subject(s) - troubleshooting , operationalization , computer science , bayesian network , inference , domain (mathematical analysis) , intelligent tutoring system , context (archaeology) , artificial intelligence , machine learning , probabilistic logic , statistical inference , bayesian inference , interdependence , bayesian probability , mathematics , statistics , mathematical analysis , paleontology , philosophy , epistemology , political science , law , biology , operating system
Probability‐based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs). Basic concepts of the approach are briefly reviewed, but the emphasis is on the considerations that arise when one attempts to operationalize the abstract framework of probability‐based reasoning in a practical ITS context. The discussion revolves around HYDRIVE, an ITS for learning to troubleshoot an aircraft hydraulics system. HYDRIVE supports generalized claims about aspects of student proficiency through probability‐based combination of rule‐based evaluations of specific actions. The paper highlights the interplay among inferential issues, the psychology of learning in the domain, and the instructional approach upon which the ITS is based.