Open AccessUSING BAYESIAN DECISION THEORY TO DESIGN A COMPUTERIZED MASTERY TEST *
Author(s) -
Lewis Charles,
Sheehan Kathleen
Publication year - 1990
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.2333-8504.1990.tb01364.x
Subject(s) - certification , test (biology) , bayesian probability , item response theory , mastery learning , computer science , criterion referenced test , bayesian statistics , subject (documents) , decision theory , test theory , computerized adaptive testing , psychology , machine learning , artificial intelligence , bayesian inference , mathematics education , standardized test , statistics , mathematics , psychometrics , paleontology , biology , clinical psychology , library science , political science , law
Mastery testing is used in educational and certification contexts to decide, on the basis of test performance, whether or not an individual has attained a specified level of knowledge, or mastery, of a given subject. A theoretical framework for mastery testing, based on Item Response Theory and Bayesian decision theory, is described in this paper. In this framework, the idea of sequential testing is developed, with the goal of providing shorter tests for individuals who have clearly mastered (or clearly not mastered) a given subject, and providing longer tests for those individuals for whom the mastery decision is not as clear‐cut. In a simulated application of the approach to a professional certification examination, it is shown that average test lengths can be reduced by half without sacrificing classification accuracy.