Machine learning, natural language processing, and psychometrics

• Other Authors: Jiao, Hong, 1968- (Editor), Lissitz, Robert W. (Editor)
• Format: Electronic eBook
• Language: English
• Published: Bingley, U.K : Emerald Publishing Limited : Information Age Publishing, Inc., [2024]
• Series: MARCES book series.
• Subjects: Machine learning.; Natural language processing (Computer science); Psychometrics.; Computers > Artificial Intelligence > Natural Language Processing.; Educational equipment and technology, computer-aided learning (CAL); Natural language and machine translation.; elektronické knihy; electronic books
• ISBN: 9781806600892
• DOI: 10.1108/9798887306063
• Physical Description: 1 online resource (242 pages) : illustrations

"With the exponential increase of digital assessment, different types of data in addition to item responses become available in the measurement process. One of the salient features in digital assessment is that process data can be easily collected. This non-conventional structured or unstructur...

Table of Contents:

• Chapter 1. The item factory: Intelligent automation in support of test development at scale / Alina A. von Davier, Andrew Runge, Yena Park, Yigal Attali, Jacqueline Church, and Geoff LaFlair. OPEN ACCESS
• Chapter 2. Applications of transformer neural networks in processing examinee responses / Susan Lottridge
• Chapter 3. Integrating natural language processing for writing assessment: Writing trait model / Paul Deane and Duanli Yan
• Chapter 4. Empirical ensemble equating under the neat design inspired by machine learning ideology / Zhehan Jiang, Yuting Han, Jihong Zhang, Lingling Xu, Dexin Shi, Haiying Liang, and Jinying Ouyang
• Chapter 5. Test security in remote testing age: Perspectives from process data analytics and ai / Jiangang Hao and Michael Fauss
• Chapter 6. Using language models to detect alarming student responses / Chirstopher Ormerod, Milan Patel, and Harry Wang
• Chapter 7. Epic analysis: Evaluating phrases in context to better understand AI scoring of essays / Steven Tang
• Chapter 8. Fully data-driven computerized adaptive testing / Aritra Ghosh, Wanyong Feng, Stephen Sireci, and Andrew Lan
• Chapter 9. From adaptive testing to personalized adaptive testing: Applications of recommender systems / Okan Bulut
• Chapter 10. AI and machine learning for next generation science assessments / Xiaoming Zhai
• Chapter 11. An ai-based platform for real time assessment, scaffolding, and alerting on students' science practices / Janice D. Gobert and the Inq-ITS Research & Development Team.