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Improving the Statistical Aspects of E-rater: Exploring Alternative Feature Reduction and Combination Rules AES

Feng, Xin; Dorans, Neil J.; Kaplan, Bruce A.
Publication Year:
Report Number:
ETS Research Report
Document Type:
Page Count:
Subject/Key Words:
Classification, Prediction, Electronic Essay Rater (E-rater), Automated Essay Scoring (AES), Automated Scoring and Natural Language Processing


quasi-uniform training sample and then validating these results in a target cross-validation sample. More research is needed in several areas. First, explicit modeling of the part of essay scores that is unrelated to word length is warranted. The POM (Proportional Odds Model) approach should be investigated in greater depth. Also needed is a statistical justification for using essay scores to score CVA variables. Algorithmic approaches to prediction/classification problem, such as boosting, may prove fruitful. Further investigation of quantile regression and ridge regression should be conducted.

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