Computer Analysis of Essay Content for Automated Score Prediction: A Prototype Automated Scoring System for GMAT Analytical Writing Assessment Essays CAT GMAT
- Author(s):
-
Burstein, Jill;
Braden-Harder, Lisa;
Chodorow, Martin;
Hua, Shuyi;
Kaplan, Bruce A.;
Kukich, Karen;
Lu, Chi;
Nolan, James;
Rock, Donald A.;
Wolff, Susanne
- Publication Year:
- 1998
- Report Number:
-
RR-98-15
- Source:
-
ETS Research Report
- Document Type:
- Report
- Page Count:
- 68
- Subject/Key Words:
-
Computer Assisted Testing,
Automation,
Electronic Essay Rater (E-rater),
Prediction,
Graduate Management Admission Test (GMAT),
Scoring,
Essay Tests
Abstract
For the set of 275 cross-validation data, exact or adjacent agreement with human rater scores reached 95%. For the 282 cross-validation issue essays, exact or adjacent agreement with human rater scores achieved 93%. The rich feature variables used as score predictors in the e-rater could potentially be used to generate explanation of score predictions, and diagnostic and instructional information.
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