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Automated Scoring of Chinese Engineering Students' English Essays
ARTICLE

, , School of Computer and Information Science, Southwest University, Chongqing, China ; , College of International Studies, Southwest University, Chongqing, China ; , School of Software Engineering, Chongqing University, Chongqing, China

IJDET Volume 15, Number 1, ISSN 1539-3100 Publisher: IGI Global

Abstract

The number of Chinese engineering students has increased greatly since 1999. Rating the quality of these students' English essays has thus become time-consuming and challenging. This paper presents a novel automatic essay scoring algorithm called PSO-SVR, based on a machine learning algorithm, Support Vector Machine for Regression (SVR), and a computational intelligence algorithm, Particle Swarm Optimization, which optimizes the parameters of SVR kernel functions. Three groups of essays, written by chemical, electrical and computer science engineering majors respectively, were used for evaluation. The study result shows that this PSO-SVR outperforms traditional essay scoring algorithms, such as multiple linear regression, support vector machine for regression and K Nearest Neighbor algorithm. It indicates that PSO-SVR is more robust in predicting irregular datasets, because the repeated use of simple content words may result in the low score of an essay, even though the system detects higher cohesion but no spelling error.

Citation

Liu, M., Wang, Y., Xu, W. & Liu, L. (2017). Automated Scoring of Chinese Engineering Students' English Essays. International Journal of Distance Education Technologies, 15(1), 52-68. IGI Global. Retrieved February 28, 2020 from .

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