Contributions of minimax theory to instructional decision making in intelligent tutoring systems.

Hendrik J. Vos

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)

Abstract

The purpose of this paper is to formulate decision rules for adapting the appropriate amount of instruction to learning needs in intelligent tutoring systems. The framework for the approach is derived from minimax decision theory (minimum information approach), i.e. optimal rules are obtained by minimizing the maximum expected loss associated with each possible decision rule. The binomial model was assumed for the conditional probability of a correct response given the true level of functioning, whereas threshold loss was adopted for the loss function involved. A simple decision rule is given for which only the minimum true level of functioning required for being a ‘true master’ and the value of the loss ratio have to be specified in advance by the decision-maker. The procedures are demonstrated for the problem of determining the optimal number of interrogatory examples for concept-learning in the Minnesota Adaptive Instructional System (MAIS). The Bayesian decision component assumed in the MAIS and the minimax strategy are compared with each other in terms of their weak and strong points. An empirical example of determining the optimal number of interrogatory examples for concept-learning in medicine concludes the paper.
Original languageUndefined
Pages (from-to)531-549
Number of pages18
JournalComputers in human behavior
Volume15
Issue number5
DOIs
Publication statusPublished - 1999

Keywords

  • Bayes rule
  • Concept-learning
  • Minimax rule
  • IR-61580
  • Intelligent Tutoring Systems
  • Instructional decision making
  • METIS-135417
  • Minnesota Adaptive Instructional System

Cite this

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title = "Contributions of minimax theory to instructional decision making in intelligent tutoring systems.",
abstract = "The purpose of this paper is to formulate decision rules for adapting the appropriate amount of instruction to learning needs in intelligent tutoring systems. The framework for the approach is derived from minimax decision theory (minimum information approach), i.e. optimal rules are obtained by minimizing the maximum expected loss associated with each possible decision rule. The binomial model was assumed for the conditional probability of a correct response given the true level of functioning, whereas threshold loss was adopted for the loss function involved. A simple decision rule is given for which only the minimum true level of functioning required for being a ‘true master’ and the value of the loss ratio have to be specified in advance by the decision-maker. The procedures are demonstrated for the problem of determining the optimal number of interrogatory examples for concept-learning in the Minnesota Adaptive Instructional System (MAIS). The Bayesian decision component assumed in the MAIS and the minimax strategy are compared with each other in terms of their weak and strong points. An empirical example of determining the optimal number of interrogatory examples for concept-learning in medicine concludes the paper.",
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Contributions of minimax theory to instructional decision making in intelligent tutoring systems. / Vos, Hendrik J.

In: Computers in human behavior, Vol. 15, No. 5, 1999, p. 531-549.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Vos, Hendrik J.

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AB - The purpose of this paper is to formulate decision rules for adapting the appropriate amount of instruction to learning needs in intelligent tutoring systems. The framework for the approach is derived from minimax decision theory (minimum information approach), i.e. optimal rules are obtained by minimizing the maximum expected loss associated with each possible decision rule. The binomial model was assumed for the conditional probability of a correct response given the true level of functioning, whereas threshold loss was adopted for the loss function involved. A simple decision rule is given for which only the minimum true level of functioning required for being a ‘true master’ and the value of the loss ratio have to be specified in advance by the decision-maker. The procedures are demonstrated for the problem of determining the optimal number of interrogatory examples for concept-learning in the Minnesota Adaptive Instructional System (MAIS). The Bayesian decision component assumed in the MAIS and the minimax strategy are compared with each other in terms of their weak and strong points. An empirical example of determining the optimal number of interrogatory examples for concept-learning in medicine concludes the paper.

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KW - Minimax rule

KW - IR-61580

KW - Intelligent Tutoring Systems

KW - Instructional decision making

KW - METIS-135417

KW - Minnesota Adaptive Instructional System

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