"Automated scoring engines [ …] require a careful balancing of the contributions of technology, NLP, psychometrics, artificial intelligence, and the learning sciences. The present handbook is evidence that the theories, methodologies, and underlying technology that surround automated scoring have reached maturity, and that there is a growing acceptance of these technologies among experts and the public."
From the Foreword by Alina von Davier, ACTNext Senior Vice President
Handbook of Automated Scoring: Theory into Practice
provides a scientifically grounded overview of the key research efforts required to move automated scoring systems into operational practice. It examines the field of automated scoring from the viewpoint of related scientific fields serving as its foundation, the latest developments of computational methodologies utilized in automated scoring, and several large-scale real-world applications of automated scoring for complex learning and assessment systems. The book is organized into three parts that cover (1) theoretical foundations, (2) operational methodologies, and (3) practical illustrations, each with a commentary. In addition, the handbook includes an introduction and synthesis chapter as well as a cross-chapter glossary.
Arvustused
'the Handbook of Automated Scoring is an excellent resource for understanding the theoretical, methodological and practical components of automated scoring. It provides a good foundation for understanding the considerations behind how assessments are designed and detailed methodological information about how to best create these kinds of systems. Part 3 that contains different illustrations of how to best design these systems is especially useful for students who are learning more about how these systems should work when implemented correctly.'
- Magdalen Beiting-Parrish and Jay Verkuilen, International Statistical Review, 2021
| Foreword |
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xi | |
| Editors |
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xv | |
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xvii | |
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1 The Past, Present, and Future of Automated Scoring |
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1 | (12) |
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Part I Theoretical Foundations |
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2 Cognitive Foundations of Automated Scoring |
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13 | (16) |
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3 Assessment Design with Automated Scoring in Mind |
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29 | (20) |
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4 Human Scoring with Automated Scoring in Mind |
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49 | (20) |
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5 Natural Language Processing for Writing and Speaking |
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69 | (24) |
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6 Multimodal Analytics for Automated Assessment |
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93 | (20) |
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7 International Applications of Automated Scoring |
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113 | (20) |
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8 Public Perception and Communication around Automated Essay Scoring |
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133 | (18) |
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9 An Evidentiary-Reasoning Perspective on Automated Scoring: Commentary on Part I |
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151 | (20) |
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Part II Operational Methodologies |
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10 Operational Human Scoring at Scale |
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171 | (24) |
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11 System Architecture Design for Scoring and Delivery |
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195 | (22) |
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12 Design and Implementation for Automated Scoring Systems |
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217 | (24) |
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13 Quality Control for Automated Scoring in Large-Scale Assessment |
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241 | (22) |
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14 A Seamless Integration of Human and Automated Scoring |
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263 | (20) |
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15 Deep Learning Networks for Automated Scoring Applications |
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283 | (14) |
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16 Validation of Automated Scoring Systems |
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297 | (22) |
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17 Operational Considerations for Automated Scoring Systems: Commentary on Part II |
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319 | (10) |
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Part III Practical Illustrations |
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18 Expanding Automated Writing Evaluation |
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329 | (18) |
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19 Automated Writing Process Analysis |
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347 | (18) |
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20 Automated Scoring of Extended Spontaneous Speech |
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365 | (18) |
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21 Conversation-Based Learning and Assessment Environments |
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383 | (20) |
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22 Automated Scoring in Intelligent Tutoring Systems |
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403 | (20) |
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23 Scoring of Streaming Data in Game-Based Assessments |
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423 | (22) |
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24 Automated Scoring in Medical Licensing |
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445 | (24) |
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25 At the Birth of the Future: Commentary on Part III |
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469 | (6) |
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26 Theory into Practice: Reflections on the Handbook |
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475 | (14) |
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| Glossary |
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489 | (12) |
| References |
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501 | (52) |
| Index |
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553 | |
Duanli Yan is Director of Data Analysis and Computational Research in the Psychometrics, Statistics, and Data Sciences area at the Educational Testing Service (ETS), and Adjunct Professor at Fordham University and Rutgers University. She is a co-author of Bayesian Networks in Educational Assessment and Computerized Adaptive and Multistage Testing with R, editor for Practical Issues and Solutions for Computerized Multistage Testing, and co-editor for Computerized Multistage Testing: Theory and Applications. Her awards include the 2016 AERA Division D Significant Contribution to Educational Measurement and Research Methodology Award.
André A. Rupp is Research Director in the Psychometrics, Statistics, and Data Sciences area at the Educational Testing Service (ETS). He is co-author and co-editor of two award-winning interdisciplinary books titled Diagnostic Measurement: Theory, Methods, and Applications and The Handbook of Cognition and Assessment: Frameworks, Methodologies, and Applications. His synthesis- and framework-oriented research has appeared in a wide variety of prestigious peer-reviewed journals. He currently serves as the lead developer of the ITEMS professional development portal for NCME.
Peter W. Foltz is Vice President in Pearson's AI and Products Solutions Organization and Research Professor at the University of Colorados Institute of Cognitive Science. His work covers machine learning and natural language processing for educational and clinical assessments, discourse processing, reading comprehension and writing skills, 21st-century skills learning, and large-scale data analytics. He has authored more than 150 journal articles, book chapters, and conference papers, as well as multiple patents.
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