Criterion-Referenced Testing: A Critical Analysis of Selected Models

Abstract

Several mathematical models for use in criterion-referenced testing are reviewed and compared. The models are evaluated on both formal-analytic and empirical grounds. Predictive models include probabilistic formulations, a binomial model, and a Bayesian model. Descriptive methods include a categorization scheme, a one-parameter logistic model, and linear regression. An empirical method for relating mastery criteria to derived educational outcomes is also included. Problems inherent in each model or class of models are described. Such problems include tenability of assumptions, ease of application, assessment of item characteristics, and assessment of the model's fit to data. Each method/model appears to be appropriate for specific types of testing situations, although further development will depend upon computer simulation and empirical research.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1978
Accession Number
ADA061569

Entities

People

  • Angelo Mirabella
  • Frederick H. Steinheiser Jr.
  • George B. Macready
  • Kenneth I. Epstein

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Bayesian Networks
  • Computational Science
  • Computer Programs
  • Computer Simulations
  • Data Science
  • Education
  • Information Science
  • Mathematical Models
  • Probabilistic Models
  • Probability
  • Psychology
  • Reliability
  • Statistical Algorithms
  • Statistical Inference
  • Students
  • Test And Evaluation

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference