Handbook of Automated Scoring

Theory into Practice
1. Auflage 2021
ISBN: 978-1-032-17347-4
Verlag: Chapman and Hall/CRC

Buch, Englisch, 580 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 872 g

Reihe: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

"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.

Autoren/Hrsg.

Yan, Duanli

Rupp, André A.

Foltz, Peter W.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

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 Lottridg

Über Autor(innen)

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.