Advancing Natural Language Processing in Educational Assessment (eBook, ePUB)

Redaktion: Yaneva, Victoria; Davier, Matthias von

• Format: ePub

Produktbeschreibung

Advancing Natural Language Processing in Educational Assessment examines the use of natural language technology in educational testing, measurement, and assessment.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 260
• Erscheinungstermin: 5. Juni 2023
• Englisch
• ISBN-13: 9781000904192
• Artikelnr.: 68124807

Autorenporträt

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.

Inhaltsangabe

Preface



by Victoria Yaneva and Matthias von Davier



Section I: Automated Scoring

Chapter 1: The Role of Robust Software in Automated Scoring



by Nitin Madnani, Aoife Cahill, and Anastassia Loukina



Chapter 2: Psychometric Considerations when Using Deep Learning for Automated Scoring



by Susan Lottridge, Chris Ormerod, and Amir Jafari



Chapter 3: Speech Analysis in Assessment



by Jared C. Bernstein and Jian Cheng



Chapter 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. Clauser



Section II: Item Development

Chapter 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 Sosoni



Chapter 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 Davier



Chapter 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 Davier



Section III: Validity and Fairness

Chapter 8: Validity, Fairness, and Technology-based Assessment



by Suzanne Lane



Chapter 9: Evaluating Fairness of Automated Scoring in Educational Measurement



by Matthew S. Johnson and Daniel F. McCaffrey



Section IV: Emerging Technologies

Chapter 10: Extracting Linguistic Signal from Item Text and Its Application to Modeling Item Characteristics



by Victoria Yaneva, Peter Baldwin, Le An Ha, and Christopher Runyon



Chapter 11: Stealth Literacy Assessment: Leveraging Games and NLP in iSTART



by Ying Fang, Laura K. Allen, Rod D. Roscoe, and Danielle S. McNamara



Chapter 12: Measuring Scientific Understanding Across International Samples: The Promise of Machine Translation and NLP-based Machine Learning Technologies



by Minsu Ha and Ross H. Nehm



Chapter 13: Making Sense of College Students' Writing Achievement and Retention with Automated Writing Evaluation



by Jill Burstein, Daniel McCaffrey, Steven Holtzman & Beata Beigman Klebanov



Contributor Biographies