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dc.contributor.editorYaneva, Victoria
dc.contributor.editorvon Davier, Matthias
dc.date.accessioned2023-05-25T13:34:11Z
dc.date.available2023-05-25T13:34:11Z
dc.date.issued2023
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/63151
dc.description.abstractAdvancing 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.en_US
dc.languageEnglishen_US
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JN Education::JND Educational systems and structures::JNDH Education: examinations and assessmenten_US
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodology::JMBT Psychological testing and measurementen_US
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JN Education::JNC Educational psychologyen_US
dc.subject.otherAdvancing 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;validityen_US
dc.titleAdvancing Natural Language Processing in Educational Assessmenten_US
dc.typebook
oapen.identifier.doi10.4324/9781003278658en_US
oapen.relation.isPublishedBy7b3c7b10-5b1e-40b3-860e-c6dd5197f0bben_US
oapen.relation.isbn9781003278658en_US
oapen.relation.isbn9781032244525en_US
oapen.relation.isbn9781032203904en_US
oapen.imprintRoutledgeen_US
oapen.pages261en_US


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