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Ramineni, Chaitanya; Williamson, David – ETS Research Report Series, 2018
Notable mean score differences for the "e-rater"® automated scoring engine and for humans for essays from certain demographic groups were observed for the "GRE"® General Test in use before the major revision of 2012, called rGRE. The use of e-rater as a check-score model with discrepancy thresholds prevented an adverse impact…
Descriptors: Scores, Computer Assisted Testing, Test Scoring Machines, Automation
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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
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Ramineni, Chaitanya; Trapani, Catherine S.; Williamson, David M.; Davey, Tim; Bridgeman, Brent – ETS Research Report Series, 2012
Automated scoring models for the "e-rater"® scoring engine were built and evaluated for the "GRE"® argument and issue-writing tasks. Prompt-specific, generic, and generic with prompt-specific intercept scoring models were built and evaluation statistics such as weighted kappas, Pearson correlations, standardized difference in…
Descriptors: Scoring, Test Scoring Machines, Automation, Models
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Attali, Yigal; Powers, Don; Freedman, Marshall; Harrison, Marissa; Obetz, Susan – ETS Research Report Series, 2008
This report describes the development, administration, and scoring of open-ended variants of GRE® Subject Test items in biology and psychology. These questions were administered in a Web-based experiment to registered examinees of the respective Subject Tests. The questions required a short answer of 1-3 sentences, and responses were automatically…
Descriptors: College Entrance Examinations, Graduate Study, Scoring, Test Construction
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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