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)


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.


Bayesian Decision Theory Classification Decisions Mastery Testing Simultaneous Optimization Compensatory Rules 


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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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