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Compensatory Rules for Optimal Classification with Mastery Scores

  • Hans J. Vos
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

A model for simultaneous optimization of combinations of test-based decisions in education and psychology is proposed using Bayesian decision theory. To illustrate the approach, one classification decision with two treatments each followed by a mastery decision are combined into a decision network. An important decision is made between weak and strong decision rules. As opposed to strong rules, weak rules are allowed to be a function of prior test scores in the series. Conditions under which optimal rules take weak monotone forms are derived. Results from a well-known problem in The Netherlands of selecting optimal continuation schools on the basis of achievement test scores are presented.

Keywords

Bayesian Decision Theory Classification Decisions Mastery Testing Simultaneous Optimization Compensatory Rules 

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References

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Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

  • Hans J. Vos
    • 1
  1. 1.Faculty of Educational Science and TechnologyUniversity of TwenteAE EnschedeThe Netherlands

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