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Download full textChen, Jing; Fife, James H.; Bejar, Isaac I.; Rupp, André A. – ETS Research Report Series, 2016
The "e-rater"® automated scoring engine used at Educational Testing Service (ETS) scores the writing quality of essays. In the current practice, e-rater scores are generated via a multiple linear regression (MLR) model as a linear combination of various features evaluated for each essay and human scores as the outcome variable. This…
Descriptors: Scoring, Models, Artificial Intelligence, Automation
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
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
