This explorative study investigates 1） whether and how quantitative measures of writing can be applied in finding out about scoring raters＇ specific tendency in their scoring of EFL writing; 2） how the knowledge of...This explorative study investigates 1） whether and how quantitative measures of writing can be applied in finding out about scoring raters＇ specific tendency in their scoring of EFL writing; 2） how the knowledge of raters＇ tendency and scoring results would help verify the best way of combining raters＇ scores; and 3） how the prediction of the writing scores of EFL writing obtained by quantitative writing performance measures would match the real scores given by raters. Based on a tentative CAF framework of writing measures, raters＇ performance or tendency in their scoring was observed and certain patterns of similarities as well as differences were found among the raters. The resuks of multiple linear regressions indicate that all raters give prior attention to the aspect of accuracy in their scoring. Differences among raters are also obvious. When it comes to the combination of different raters＇ scores, the study also finds that weighted average is the best of the three ways of combining scores for this group of raters because it has yielded the best predicting scores than the ＂pure average＂. It is even slightly better than the results obtained by facet analysis in terms of some important indices such as R square and Durbin-Watson value. The matching of the predicted scores with the real scores is well over 50 percent. The results of the study are further discussed in relation to the application of wpm and the possible improvement of wpm framework. The methodological, theoretical and practical implications of the study have also been touched upon in the relevant part of the article.展开更多
In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification...In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification accuracy. To evaluate the prediction accuracy of the model, we compare its performance with those of SVM, linear discriminate analysis, logistic regression analysis, K-nearest neighbors, classification and regression tree, neural network and PCA-NN. The experimental results show the model have a very good prediction accuracy展开更多
基金funded by China National Planning Office of Philosophy and Social Science(No.08XYY007)
文摘This explorative study investigates 1） whether and how quantitative measures of writing can be applied in finding out about scoring raters＇ specific tendency in their scoring of EFL writing; 2） how the knowledge of raters＇ tendency and scoring results would help verify the best way of combining raters＇ scores; and 3） how the prediction of the writing scores of EFL writing obtained by quantitative writing performance measures would match the real scores given by raters. Based on a tentative CAF framework of writing measures, raters＇ performance or tendency in their scoring was observed and certain patterns of similarities as well as differences were found among the raters. The resuks of multiple linear regressions indicate that all raters give prior attention to the aspect of accuracy in their scoring. Differences among raters are also obvious. When it comes to the combination of different raters＇ scores, the study also finds that weighted average is the best of the three ways of combining scores for this group of raters because it has yielded the best predicting scores than the ＂pure average＂. It is even slightly better than the results obtained by facet analysis in terms of some important indices such as R square and Durbin-Watson value. The matching of the predicted scores with the real scores is well over 50 percent. The results of the study are further discussed in relation to the application of wpm and the possible improvement of wpm framework. The methodological, theoretical and practical implications of the study have also been touched upon in the relevant part of the article.
文摘In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification accuracy. To evaluate the prediction accuracy of the model, we compare its performance with those of SVM, linear discriminate analysis, logistic regression analysis, K-nearest neighbors, classification and regression tree, neural network and PCA-NN. The experimental results show the model have a very good prediction accuracy
