@Article{cmc.2020.010471,
AUTHOR = {Shuai Yuan, Tingting He, Huan Huang, Rui Hou, Meng Wang},
TITLE = {Automated Chinese Essay Scoring Based on Deep Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {817--833},
URL = {http://www.techscience.com/cmc/v65n1/39597},
ISSN = {1546-2226},
ABSTRACT = {Writing is an important part of language learning and is considered the best 
approach to demonstrate the comprehensive language skills of students. Manually 
grading student essays is a time-consuming task; however, it is necessary. An automated 
essay scoring system can not only greatly improve the efficiency of essay scoring, but 
also provide more objective score. Therefore, many researchers have been exploring 
automated essay scoring techniques and tools. However, the technique of scoring Chinese 
essays is still limited, and its accuracy needs to be enhanced further. To improve the 
accuracy of the scoring model for a Chinese essay, we propose an automated scoring 
approach based on a deep learning model and validate its effect by conducting two 
comparison experiments. The experimental results indicate that the accuracy of the 
proposed model is significantly higher than that of multiple linear regression (MLR), 
which was commonly used in the past. The three accuracy rates of the proposed model 
are comparable to those of the novice teacher. The root mean square error (RMSE) of the 
proposed model is slightly lower than that of the novice teacher, and the correlation 
coefficient of the proposed model is also significantly higher than that of the novice 
teacher. Besides, when the predicted scores are not very low or very high, the two 
predicted models are as good as a novice teacher. However, when the predicted score is 
very high or very low, the results should be treated with caution.},
DOI = {10.32604/cmc.2020.010471}
}