Building e-rater Scoring Models Using Machine Learning Methods AES MLR
- Author(s):
-
Chen, Jing;
Fife, James H.;
Bejar, Isaac I.;
Rupp, André A.
- Publication Year:
- 2016
- Report Number:
-
RR-16-04
- Source:
-
ETS Research Report
- Document Type:
- Report
- Page Count:
- 14
- Subject/Key Words:
-
Machine Learning,
Automated Essay Scoring (AES),
Linear Regression,
Statistical Modeling,
e-rater,
Algorithms,
Human Computer Agreement
Abstract
Furthermore, compared with MLR, SVM-based models improved the agreement between human and e-rater scores at the ends of the score scale. In addition, the high correlation between SVM-based e-rater scores with external measures such as examinee's scores on the other parts of the test provided some validity evidence for SVM-based e-rater scores. Future research is encouraged to explore the generalizability of these findings.
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