Prediction of Essay Scores From Writing Process and Product Features Using Data Mining Methods

被引:36
作者
Sinharay, Sandip [1 ]
Zhang, Mo [1 ]
Deane, Paul [1 ]
机构
[1] Educ Testing Serv, Res & Dev, 660 Rosedale Rd, Princeton, NJ 08541 USA
关键词
WORKING-MEMORY; WRITTEN COMPOSITION; CLASSIFICATION;
D O I
10.1080/08957347.2019.1577245
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Analysis of keystroke logging data is of increasing interest, as evident from a substantial amount of recent research on the topic. Some of the research on keystroke logging data has focused on the prediction of essay scores from keystroke logging features, but linear regression is the only prediction method that has been used in this research. Data mining methods such as boosting and random forests have been found to improve over traditional prediction methods such as linear regression in various scientific fields, but have not been used in the prediction of essay scores from keystroke logging features. This article first provides a review of boosting, which is a popular data mining method. The article then applies boosting to predict essay scores from a large number of keystroke logging features and other predictor variables from two real data sets.
引用
收藏
页码:116 / 137
页数:22
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