A Bayesian sequential procedure for determining the optimal number of interrogatory examples for concept learning

Hendrik J. Vos

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)

Abstract

The purpose of this paper is to derive optimal rules for sequential decision-making in intelligent tutoring systems. In a sequential mastery test, the decision is to classify a student as a master, a nonmaster, or to continue testing and administering another item. The framework of Bayesian sequential decision theory is used; that is, optimal rules are obtained by minimizing the posterior expected losses associated with all possible decision rules at each stage of testing and using techniques of backward induction. The main advantage of this approach is that costs of testing can be taken explicitly into account. The sequential testing procedure is demonstrated for determining the optimal number of interrogatory examples for concept-learning in the Minnesota adaptive instructional system. The paper concludes with an empirical example in which, for given maximum number of interrogatory examples for concept-learning in medicine, the appropriate action is indicated at each stage of testing for different number-correct score.
Original languageUndefined
Pages (from-to)609-627
Number of pages18
JournalComputers in human behavior
Volume23
Issue number1
DOIs
Publication statusPublished - 2007

Keywords

  • IR-61579
  • METIS-243961

Cite this

@article{42f10ed216ba4d909dcd7d6053671a24,
title = "A Bayesian sequential procedure for determining the optimal number of interrogatory examples for concept learning",
abstract = "The purpose of this paper is to derive optimal rules for sequential decision-making in intelligent tutoring systems. In a sequential mastery test, the decision is to classify a student as a master, a nonmaster, or to continue testing and administering another item. The framework of Bayesian sequential decision theory is used; that is, optimal rules are obtained by minimizing the posterior expected losses associated with all possible decision rules at each stage of testing and using techniques of backward induction. The main advantage of this approach is that costs of testing can be taken explicitly into account. The sequential testing procedure is demonstrated for determining the optimal number of interrogatory examples for concept-learning in the Minnesota adaptive instructional system. The paper concludes with an empirical example in which, for given maximum number of interrogatory examples for concept-learning in medicine, the appropriate action is indicated at each stage of testing for different number-correct score.",
keywords = "IR-61579, METIS-243961",
author = "Vos, {Hendrik J.}",
year = "2007",
doi = "10.1016/j.chb.2004.11.002",
language = "Undefined",
volume = "23",
pages = "609--627",
journal = "Computers in human behavior",
issn = "0747-5632",
publisher = "Elsevier",
number = "1",

}

A Bayesian sequential procedure for determining the optimal number of interrogatory examples for concept learning. / Vos, Hendrik J.

In: Computers in human behavior, Vol. 23, No. 1, 2007, p. 609-627.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A Bayesian sequential procedure for determining the optimal number of interrogatory examples for concept learning

AU - Vos, Hendrik J.

PY - 2007

Y1 - 2007

N2 - The purpose of this paper is to derive optimal rules for sequential decision-making in intelligent tutoring systems. In a sequential mastery test, the decision is to classify a student as a master, a nonmaster, or to continue testing and administering another item. The framework of Bayesian sequential decision theory is used; that is, optimal rules are obtained by minimizing the posterior expected losses associated with all possible decision rules at each stage of testing and using techniques of backward induction. The main advantage of this approach is that costs of testing can be taken explicitly into account. The sequential testing procedure is demonstrated for determining the optimal number of interrogatory examples for concept-learning in the Minnesota adaptive instructional system. The paper concludes with an empirical example in which, for given maximum number of interrogatory examples for concept-learning in medicine, the appropriate action is indicated at each stage of testing for different number-correct score.

AB - The purpose of this paper is to derive optimal rules for sequential decision-making in intelligent tutoring systems. In a sequential mastery test, the decision is to classify a student as a master, a nonmaster, or to continue testing and administering another item. The framework of Bayesian sequential decision theory is used; that is, optimal rules are obtained by minimizing the posterior expected losses associated with all possible decision rules at each stage of testing and using techniques of backward induction. The main advantage of this approach is that costs of testing can be taken explicitly into account. The sequential testing procedure is demonstrated for determining the optimal number of interrogatory examples for concept-learning in the Minnesota adaptive instructional system. The paper concludes with an empirical example in which, for given maximum number of interrogatory examples for concept-learning in medicine, the appropriate action is indicated at each stage of testing for different number-correct score.

KW - IR-61579

KW - METIS-243961

U2 - 10.1016/j.chb.2004.11.002

DO - 10.1016/j.chb.2004.11.002

M3 - Article

VL - 23

SP - 609

EP - 627

JO - Computers in human behavior

JF - Computers in human behavior

SN - 0747-5632

IS - 1

ER -