Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 3 |
Descriptor
Classification | 3 |
Natural Language Processing | 3 |
Test Items | 3 |
Automation | 2 |
Models | 2 |
Artificial Intelligence | 1 |
College Entrance Examinations | 1 |
Computer Assisted Testing | 1 |
Generalization | 1 |
Grading | 1 |
Graduate Study | 1 |
More ▼ |
Author
Becker, Kirk A. | 1 |
Condor, Aubrey | 1 |
Futagi, Yoko | 1 |
Hemat, Ramin | 1 |
Kao, Shu-chuan | 1 |
Kostin, Irene | 1 |
Litster, Max | 1 |
Pardos, Zachary | 1 |
Sheehan, Kathleen M. | 1 |
Zuckerman, Daniel | 1 |
Publication Type
Reports - Research | 3 |
Journal Articles | 2 |
Numerical/Quantitative Data | 1 |
Speeches/Meeting Papers | 1 |
Education Level
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
Graduate Record Examinations | 1 |
What Works Clearinghouse Rating
Condor, Aubrey; Litster, Max; Pardos, Zachary – International Educational Data Mining Society, 2021
We explore how different components of an Automatic Short Answer Grading (ASAG) model affect the model's ability to generalize to questions outside of those used for training. For supervised automatic grading models, human ratings are primarily used as ground truth labels. Producing such ratings can be resource heavy, as subject matter experts…
Descriptors: Automation, Grading, Test Items, Generalization
Becker, Kirk A.; Kao, Shu-chuan – Journal of Applied Testing Technology, 2022
Natural Language Processing (NLP) offers methods for understanding and quantifying the similarity between written documents. Within the testing industry these methods have been used for automatic item generation, automated scoring of text and speech, modeling item characteristics, automatic question answering, machine translation, and automated…
Descriptors: Item Banks, Natural Language Processing, Computer Assisted Testing, Scoring
Sheehan, Kathleen M.; Kostin, Irene; Futagi, Yoko; Hemat, Ramin; Zuckerman, Daniel – ETS Research Report Series, 2006
This paper describes the development, implementation, and evaluation of an automated system for predicting the acceptability status of candidate reading-comprehension stimuli extracted from a database of journal and magazine articles. The system uses a combination of classification and regression techniques to predict the probability that a given…
Descriptors: Automation, Prediction, Reading Comprehension, Classification