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Advancing Natural Language Processing in Educational Assessment

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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Format: Online
Jezik:engleski
Izdano: Taylor & Francis 2023
Teme:
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
thema EDItEUR::J Society and Social Sciences::JN Education::JND Educational systems and structures::JNDH Education: examinations and assessment
thema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology::JMBT Psychological testing and measurement
thema EDItEUR::J Society and Social Sciences::JN Education::JNC Educational psychology
Online pristup:https://library.oapen.org/handle/20.500.12657/63151
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Sažetak: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.

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