1st Edition

Handbook of Automated Scoring Theory into Practice

Edited By Duanli Yan, André A. Rupp, Peter W. Foltz Copyright 2020
    580 Pages
    by Chapman & Hall

    580 Pages
    by Chapman & Hall

    "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.

    Contents

    Foreword..................................................................................................................xi

    1 The Past, Present, and Future of Automated Scoring..............................1

    Peter W. Foltz, Duanli Yan, and André A. Rupp

    Part I: Theoretical Foundations

    2 Cognitive Foundations of Automated Scoring........................................ 13

    Malcolm I. Bauer and Diego Zapata-Rivera

    3 Assessment Design with Automated Scoring in Mind.........................29

    Kristen DiCerbo, Emily Lai, and Matthew Ventura

    4 Human Scoring with Automated Scoring in Mind................................49

    Edward W. Wolfe

    5 Natural Language Processing for Writing and Speaking..................... 69

    Aoife Cahill and Keelan Evanini

    6 Multimodal Analytics for Automated Assessment................................93

    Sidney K. D’Mello

    7 International Applications of Automated Essay Scoring.................... 113

    Mark D. Shermis

    8 Public Perception and Communication around Automated

    Essay Scoring................................................................................................ 133

    Scott W. Wood

    9 An Evidentiary-Reasoning Perspective on Automated Scoring:

    Commentary on Part I................................................................................ 151

    Robert J. Mislevy

    Part II: Operational Methodologies

    10 Operational Human Scoring at Scale...................................................... 171

    Kathryn L. Ricker-Pedley, Susan Hines, and Carolyn Connelly

    11 System Architecture Design for Scoring and Delivery....................... 195

    Sue Lottridge and Nick Hoefer

    12 Design and Implementation for Automated

    Scoring Systems........................................................................................... 217

    Christina Schneider and Michelle Boyer

    13 Quality Control for Automated Scoring in Large-Scale

    Assessment.................................................................................................... 241

    Dan Shaw, Brad Bolender, and Rick Meisner

    14 A Seamless Integration of Human and Automated Scoring..............263

    Kyle Habermehl, Aditya Nagarajan, and Scott Dooley

    15 Deep Learning Networks for Automated Scoring Applications.......283

    Saad M. Khan and Yuchi Huang

    16 Validation of Automated Scoring Systems............................................ 297

    Duanli Yan and Brent Bridgeman

    17 Operational Considerations for Automated Scoring Systems:

    Commentary on Part II............................................................................... 319

    David M. Williamson

    Part III: Practical Illustrations

    18 Expanding Automated Writing Evaluation............................................ 329

    Jill Burstein, Brian Riordan, and Daniel McCaffrey

    19 Automated Writing Process Analysis.....................................................347

    Paul Deane and Mo Zhang

    20 Automated Scoring of Extended Spontaneous Speech.......................365

    Klaus Zechner and Anastassia Loukina

    21 Conversation-Based Learning and Assessment Environments.........383

    Arthur C. Graesser, Xiangen Hu, Vasile Rus, and Zhiqiang Cai

    22 Automated Scoring in Intelligent Tutoring Systems...........................403

    Robert J. Mislevy, Duanli Yan, Janice Gobert, and Michael Sao Pedro

    23 Scoring of Streaming Data in Game-Based Assessments...................423

    Russell G. Almond

    24 Automated Scoring in Medical Licensing..............................................445

    Melissa J. Margolis and Brian E. Clauser

    25 At the Birth of the Future: Commentary on Part III............................. 469

    John T. Behrens

    26 Theory into Practice: Reflections on the Handbook............................. 475

    Andre A. Rupp, Peter W. Foltz, and Duanli Yan

    Glossary................................................................................................................489

    References............................................................................................................ 501

    Index......................................................................................................................553

    Biography

    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 Colorado’s 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.