ERIC Number: ED389746
Record Type: RIE
Publication Date: 1994-Nov
Reference Count: N/A
Applications of Bayesian Decision Theory to Intelligent Tutoring Systems. Research Report 94-16.
Vos, Hans J.
Some applications of Bayesian decision theory to intelligent tutoring systems are considered. How the problem of adapting the appropriate amount of instruction to the changing nature of a student's capabilities during the learning process can be situated in the general framework of Bayesian decision theory is discussed in the context of the Minnesota Adaptive Instructional System (MAIS). Two basic elements of this approach are used to improve instructional decision making in intelligent tutoring systems. First, it is argued that in many decision-making situations the linear loss model is a realistic representation of the losses actually incurred. Second, it is shown that the psychometric model relating observed test scores to the true level of functioning can be represented by Kelley's regression line from classical test theory. Optimal decision rules for the MAIS are derived using these two features. (Contains 3 tables, 1 figure, and 42 references.) (SLD)
Descriptors: Bayesian Statistics, Decision Making, Foreign Countries, Intelligent Tutoring Systems, Models, Psychometrics, Scores, Test Results, Test Theory
Bibliotheek, Faculty of Educational Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.
Publication Type: Reports - Evaluative
Education Level: N/A
Authoring Institution: Twente Univ., Enschede (Netherlands). Faculty of Educational Science and Technology.
Identifiers: Decision Theory; Minnesota Adaptive Instructional System