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Yao, Lili; Haberman, Shelby J.; Zhang, Mo – ETS Research Report Series, 2019
Many assessments of writing proficiency that aid in making high-stakes decisions consist of several essay tasks evaluated by a combination of human holistic scores and computer-generated scores for essay features such as the rate of grammatical errors per word. Under typical conditions, a summary writing score is provided by a linear combination…
Descriptors: Prediction, True Scores, Computer Assisted Testing, Scoring
Breyer, F. Jay; Attali, Yigal; Williamson, David M.; Ridolfi-McCulla, Laura; Ramineni, Chaitanya; Duchnowski, Matthew; Harris, April – ETS Research Report Series, 2014
In this research, we investigated the feasibility of implementing the "e-rater"® scoring engine as a check score in place of all-human scoring for the "Graduate Record Examinations"® ("GRE"®) revised General Test (rGRE) Analytical Writing measure. This report provides the scientific basis for the use of e-rater as a…
Descriptors: Computer Software, Computer Assisted Testing, Scoring, College Entrance Examinations
Attali, Yigal; Bridgeman, Brent; Trapani, Catherine – Journal of Technology, Learning, and Assessment, 2010
A generic approach in automated essay scoring produces scores that have the same meaning across all prompts, existing or new, of a writing assessment. This is accomplished by using a single set of linguistic indicators (or features), a consistent way of combining and weighting these features into essay scores, and a focus on features that are not…
Descriptors: Writing Evaluation, Writing Tests, Scoring, Test Scoring Machines