Del i serien NCME APPLICATIONS OF EDUCATIONAL MEASUREMENT AND ASSESSMENT

Advancing Natural Language Processing in Educational Assessment

Häftad, Engelska, 2023

859 kr

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Advancing Natural Language Processing in Educational Assessment examines the use of natural language technology in educational testing, measurement, and assessment. Recent developments in natural language processing (NLP) have enabled large-scale educational applications, though scholars and professionals may lack a shared understanding of the strengths and limitations of NLP in assessment as well as the challenges that testing organizations face in implementation. This first-of-its-kind book provides evidence-based practices for the use of NLP-based approaches to automated text and speech scoring, language proficiency assessment, technology-assisted item generation, gamification, learner feedback, and beyond. Spanning historical context, validity and fairness issues, emerging technologies, and implications for feedback and personalization, these chapters represent the most robust treatment yet about NLP for education measurement researchers, psychometricians, testing professionals, and policymakers.The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-NonCommercial-No Derivatives 4.0 license.

Produktinformation

Utgivningsdatum2023-06-05
Mått178 x 254 x 18 mm
Vikt217 g
FormatHäftad
SpråkEngelska
SerieNCME APPLICATIONS OF EDUCATIONAL MEASUREMENT AND ASSESSMENT
Antal sidor250
FörlagTaylor & Francis Ltd
ISBN9781032244525

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Preface by Victoria Yaneva and Matthias von DavierSection I: Automated ScoringChapter 1: The Role of Robust Software in Automated Scoring by Nitin Madnani, Aoife Cahill, and Anastassia LoukinaChapter 2: Psychometric Considerations when Using Deep Learning for Automated Scoring by Susan Lottridge, Chris Ormerod, and Amir JafariChapter 3: Speech Analysis in Assessment by Jared C. Bernstein and Jian ChengChapter 4: Assessment of Clinical Skills: A Case Study in Constructing an NLP-Based Scoring System for Patient Notes by Polina Harik, Janet Mee, Christopher Runyon, and Brian E. ClauserSection II: Item DevelopmentChapter 5: Automatic Generation of Multiple-Choice Test Items from Paragraphs Using Deep Neural Networks by Ruslan Mitkov, Le An Ha, Halyna Maslak, Tharindu Ranasinghe, and Vilelmini SosoniChapter 6: Training Optimus Prime, M.D.: A Case Study of Automated Item Generation using Artificial Intelligence – From Fine-Tuned GPT2 to GPT3 and Beyond by Matthias von DavierChapter 7: Computational Psychometrics for Digital-first Assessments: A Blend of ML and Psychometrics for Item Generation and Scoring by Geoff LaFlair, Kevin Yancey, Burr Settles, Alina A von DavierSection III: Validity and FairnessChapter 8: Validity, Fairness, and Technology-based Assessment by Suzanne LaneChapter 9: Evaluating Fairness of Automated Scoring in Educational Measurement by Matthew S. Johnson and Daniel F. McCaffreySection IV: Emerging TechnologiesChapter 10: Extracting Linguistic Signal from Item Text and Its Application to Modeling Item Characteristics by Victoria Yaneva, Peter Baldwin, Le An Ha, and Christopher RunyonChapter 11: Stealth Literacy Assessment: Leveraging Games and NLP in iSTART by Ying Fang, Laura K. Allen, Rod D. Roscoe, and Danielle S. McNamaraChapter 12: Measuring Scientific Understanding Across International Samples: The Promise of Machine Translation and NLP-based Machine Learning Technologies by Minsu Ha and Ross H. NehmChapter 13: Making Sense of College Students’ Writing Achievement and Retention with Automated Writing Evaluation by Jill Burstein, Daniel McCaffrey, Steven Holtzman & Beata Beigman KlebanovContributor Biographies

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