The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

被引：10
|
作者：
Haberman, Shelby J. [1 ]
Sinharay, Sandip [1 ]
机构：
[1] Educ Testing Serv, Princeton, NJ 08541 USA
来源：
JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS | 2010年 / 35卷 / 05期
关键词：
deleted residual; cross-validation; PRESS; regression; CONCAVITY; AGREEMENT;
D O I：
10.3102/1076998610375839
中图分类号：
G40 [教育学];
学科分类号：
040101 ; 120403 ;
摘要：
Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a large variety of data sets. It appears that the cumulative logit model performed somewhat better than did the linear regression model.
引用
收藏
页码：586 / 602
页数：17
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