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Advancing Natural Language Processing in Educational Assessment

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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 profess...

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Bibliographic Details
Other Authors: Yaneva, Victoria (Editor), von Davier, Matthias (Editor)
Format: Electronic Book Chapter
Language:English
Published: Taylor & Francis 2023
Subjects:
Examinations & assessment
Psychological testing & measurement
Educational psychology
Advancing Natural Language Processing in Educational Assessment;artificial intelligence;automated text and speech scoring;automatic item generation;classroom assessment;deep neural networks;educational measurement;educational testing;fairness;gamification;language proficiency assessment;learner feedback;linguistic signal;Matthias von Davier;multiple choice items;National Board of Medical Examiners;National Council on Measurement in Education;NBME;NCME;NLP;personalized learning;psychometrics;technology-assisted item generation;Victoria Yaneva;validity
Online Access:DOAB: download the publication
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Summary: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.
Physical Description:1 electronic resource (261 p.)
ISBN:9781003278658
9781032244525
9781032203904
Access:Open Access

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