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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- http://dx.doi.org/10.1002/ets2.12094