Applications of Bayesian decision theory to intelligent tutoring systems

Hans J. Vos

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)
245 Downloads (Pure)


The purpose of this paper is to consider some applications of Bayesian decision theory to intelligent tutoring systems. In particular, it will be indicated how the problem of adapting the appropriate amount of instruction to the changing nature of student's capabilities during the learning process can be situated within the general framework of Bayesian decision theory. Two basic elements of this approach will be 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 will be derived using these two features.
Original languageEnglish
Pages (from-to)149-162
JournalComputers in human behavior
Issue number1
Publication statusPublished - 1995


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