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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
Quinlan, Thomas; Higgins, Derrick; Wolff, Susanne – Educational Testing Service, 2009
This report evaluates the construct coverage of the e-rater[R[ scoring engine. The matter of construct coverage depends on whether one defines writing skill, in terms of process or product. Originally, the e-rater engine consisted of a large set of components with a proven ability to predict human holistic scores. By organizing these capabilities…
Descriptors: Guides, Writing Skills, Factor Analysis, Writing Tests
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Gorin, Joanna S.; Embretson, Susan E. – Applied Psychological Measurement, 2006
Recent assessment research joining cognitive psychology and psychometric theory has introduced a new technology, item generation. In algorithmic item generation, items are systematically created based on specific combinations of features that underlie the processing required to correctly solve a problem. Reading comprehension items have been more…
Descriptors: Difficulty Level, Test Items, Modeling (Psychology), Paragraph Composition