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McCaffrey, Daniel F.; Zhang, Mo; Burstein, Jill – Grantee Submission, 2022
Background: This exploratory writing analytics study uses argumentative writing samples from two performance contexts--standardized writing assessments and university English course writing assignments--to compare: (1) linguistic features in argumentative writing; and (2) relationships between linguistic characteristics and academic performance…
Descriptors: Persuasive Discourse, Academic Language, Writing (Composition), Academic Achievement
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Zhang, Mo; Sinharay, Sandip – International Journal of Testing, 2022
This article demonstrates how recent advances in technology allow fine-grained analyses of candidate-produced essays, thus providing a deeper insight on writing performance. We examined how essay features, automatically extracted using natural language processing and keystroke logging techniques, can predict various performance measures using data…
Descriptors: At Risk Persons, Writing Achievement, Educational Technology, Writing Improvement
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Zhang, Mo; Chen, Jing; Ruan, Chunyi – ETS Research Report Series, 2016
Successful detection of unusual responses is critical for using machine scoring in the assessment context. This study evaluated the utility of approaches to detecting unusual responses in automated essay scoring. Two research questions were pursued. One question concerned the performance of various prescreening advisory flags, and the other…
Descriptors: Essays, Scoring, Automation, Test Scoring Machines
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Loukina, Anastassia; Zechner, Klaus; Yoon, Su-Youn; Zhang, Mo; Tao, Jidong; Wang, Xinhao; Lee, Chong Min; Mulholland, Matthew – ETS Research Report Series, 2017
This report presents an overview of the "SpeechRater"? automated scoring engine model building and evaluation process for several item types with a focus on a low-English-proficiency test-taker population. We discuss each stage of speech scoring, including automatic speech recognition, filtering models for nonscorable responses, and…
Descriptors: Automation, Scoring, Speech Tests, Test Items
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Chen, Jing; Zhang, Mo; Bejar, Isaac I. – ETS Research Report Series, 2017
Automated essay scoring (AES) generally computes essay scores as a function of macrofeatures derived from a set of microfeatures extracted from the text using natural language processing (NLP). In the "e-rater"® automated scoring engine, developed at "Educational Testing Service" (ETS) for the automated scoring of essays, each…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essay Tests
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Zhang, Mo; Williamson, David M.; Breyer, F. Jay; Trapani, Catherine – International Journal of Testing, 2012
This article describes two separate, related studies that provide insight into the effectiveness of "e-rater" score calibration methods based on different distributional targets. In the first study, we developed and evaluated a new type of "e-rater" scoring model that was cost-effective and applicable under conditions of absent human rating and…
Descriptors: Automation, Scoring, Models, Essay Tests
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Zhang, Mo – ETS Research Report Series, 2013
Many testing programs use automated scoring to grade essays. One issue in automated essay scoring that has not been examined adequately is population invariance and its causes. The primary purpose of this study was to investigate the impact of sampling in model calibration on population invariance of automated scores. This study analyzed scores…
Descriptors: Automation, Scoring, Essay Tests, Sampling