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Bayesian Networks in Educational Assessment

  • Russell G. Almond
  • Robert J. Mislevy
  • Linda S. Steinberg
  • Duanli Yan
  • David M. Williamson

Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Table of contents

  1. Front Matter
    Pages I-XXXIII
  2. Building Blocks for Bayesian Networks

    1. Front Matter
      Pages 1-1
    2. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 3-18
    3. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 19-40
    4. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 41-79
    5. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 81-103
    6. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 105-155
    7. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 157-195
    8. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 197-237
  3. Learning and Revising Models from Data

    1. Front Matter
      Pages 239-239
    2. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 241-278
    3. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 279-330
    4. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 331-369
    5. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 371-407
  4. Evidence-Centered Assessment Design

    1. Front Matter
      Pages 409-409
    2. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 411-465
    3. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 467-505
    4. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 507-547
    5. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 549-582
    6. Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson
      Pages 583-599
  5. Back Matter
    Pages 601-662

About this book

Introduction

Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments.

Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics.

This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.

Keywords

Artificial Intelligence Bayes net Bayesian model education ECD Evidence-Centered Design Evidence-centered assessment design Uncertainty

Authors and affiliations

  • Russell G. Almond
    • 1
  • Robert J. Mislevy
    • 2
  • Linda S. Steinberg
    • 3
  • Duanli Yan
    • 4
  • David M. Williamson
    • 5
  1. 1.Florida State UniversityTallahasseeUSA
  2. 2.Educational Testing ServicePrincetonUSA
  3. 3.PenningtonPenningtonUSA
  4. 4.Educational Testing ServicePrincetonUSA
  5. 5.Educational Testing ServicePrincetonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-2125-6
  • Copyright Information Springer Science+Business Media New York 2015
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4939-2124-9
  • Online ISBN 978-1-4939-2125-6
  • Series Print ISSN 2199-7357
  • Series Online ISSN 2199-7365
  • Buy this book on publisher's site