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Advancing Natural Language Processing in Educational Assessment

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Advancing Natural Language Processing in Educational Assessment
artificial intelligence
ASEs
ASR System
Automated Essay Scoring
Automated Scoring Algorithms
automated scoring for teacher development
Automated Scoring System
automated text and speech scoring
Automatic Item Generation
Automatic Scoring
Category=JMA
Category=JNC
Category=JNDH
Category=JNM
Category=JNT
Category=UYQL
classroom assessment
Construct Irrelevant Variance
deep learning applications
Deep Learning Models
deep neural networks
digital classroom innovation
educational data mining
educational measurement
educational testing
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
F1 Score
fairness
formative assessment tools
gamification
High School GPA
Human Scores
language proficiency assessment
language proficiency evaluation
learner feedback
linguistic signal
Machine Scores
Matthias von Davier
MT
multiple choice items
Multiple Choice Test Items
National Board of Medical Examiners
National Council on Measurement in Education
NBME
NCME
NLP
NLP Method
NLP Tool
personalized learning
Predicting Item Difficulty
Pretrained Models
psychometric modeling
psychometrics
Reading Test Scores
Stealth Assessments
technology-assisted item generation
USMLE Step
validity
Victoria Yaneva
Von Davier

Product details

  • ISBN 9781032203904
  • Weight: 453g
  • Dimensions: 178 x 254mm
  • Publication Date: 05 Jun 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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.
About

Victoria Yaneva is Senior NLP Scientist at the National Board of Medical Examiners, USA.

Matthias von Davier is Monan Professor of Education in the Lynch School of Education and Executive Director of TIMSS & PIRLS International Study Center at Boston College, USA.