Open Access
ARTICLE
Automated Chinese Essay Scoring Based on Deep Learning
Shuai Yuan1, Tingting He2, 3, *, Huan Huang4, Rui Hou5, Meng Wang6
1 National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China.
2 School of Computer, Central China Normal University, Wuhan, 430079, China.
3 Information Retrieval and Knowledge Management Research Laboratory, Central China Normal University, Wuhan, 430079, China.
4 School of Education, South-Central University for Nationalities, Wuhan, 430074, China.
5 College of Computer Science, South-Central University for Nationalities, Wuhan, 430074, China.
6 Information School, University of Washington, Seattle, WA98105, USA.
* Corresponding Author: Tingting He. Email: .
Computers, Materials & Continua 2020, 65(1), 817-833. https://doi.org/10.32604/cmc.2020.010471
Received 06 March 2020; Accepted 02 June 2020; Issue published 23 July 2020
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.
Keywords
Cite This Article
S. Yuan, T. He, H. Huang, R. Hou and M. Wang, "Automated chinese essay scoring based on deep learning,"
Computers, Materials & Continua, vol. 65, no.1, pp. 817–833, 2020. https://doi.org/10.32604/cmc.2020.010471
Citations