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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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Bejar, Isaac I.; VanWinkle, Waverely; Madnani, Nitin; Lewis, William; Steier, Michael – ETS Research Report Series, 2013
The paper applies a natural language computational tool to study a potential construct-irrelevant response strategy, namely the use of "shell language." Although the study is motivated by the impending increase in the volume of scoring of students responses from assessments to be developed in response to the Race to the Top initiative,…
Descriptors: Responses, Language Usage, Natural Language Processing, Computational Linguistics
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Deane, Paul; Lawless, René R.; Li, Chen; Sabatini, John; Bejar, Isaac I.; O'Reilly, Tenaha – ETS Research Report Series, 2014
We expect that word knowledge accumulates gradually. This article draws on earlier approaches to assessing depth, but focuses on one dimension: richness of semantic knowledge. We present results from a study in which three distinct item types were developed at three levels of depth: knowledge of common usage patterns, knowledge of broad topical…
Descriptors: Vocabulary, Test Items, Language Tests, Semantics