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Dynamic Bayesian networks (DBNs; Reye, 2004) are a promising tool for modeling student proficiency under rich measurement scenarios (Reichenberg, in press). These scenarios often present assessment conditions far more complex

Dynamic Bayesian networks (DBNs; Reye, 2004) are a promising tool for modeling student proficiency under rich measurement scenarios (Reichenberg, in press). These scenarios often present assessment conditions far more complex than what is seen with more traditional assessments and require assessment arguments and psychometric models capable of integrating those complexities. Unfortunately, DBNs remain understudied and their psychometric properties relatively unknown.

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Date Created
  • 2018
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  • Text
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    Note
    • Partial requirement for: Ph.D., Arizona State University, 2018
      Note type
      thesis
    • Includes bibliographical references (pages 111-120)
      Note type
      bibliography
    • Field of study: Family and human development

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    by Raymond E. Reichenberg

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