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Automated Scoring of Chinese Engineering Students' English Essays
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Author(s): Ming Liu (School of Computer and Information Science, Southwest University, Chongqing, China), Yuqi Wang (School of Computer and Information Science, Southwest University, Chongqing, China), Weiwei Xu (College of International Studies, Southwest University, Chongqing, China)and Li Liu (School of Software Engineering, Chongqing University, Chongqing, China)
Copyright: 2017
Volume: 15
Issue: 1
Pages: 17
Source title:
International Journal of Distance Education Technologies (IJDET)
Editor(s)-in-Chief: Maiga Chang (Athabasca University, Canada)
DOI: 10.4018/IJDET.2017010104
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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.
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