Automated Scoring of Chinese Engineering Students' English Essays
Պահպանված է:
| Հրատարակված է: | International Journal of Distance Education Technologies vol. 15, no. 1 (2017), p. 52 |
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| Հիմնական հեղինակ: | |
| Այլ հեղինակներ: | , , |
| Հրապարակվել է: |
IGI Global
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| Խորագրեր: | |
| Առցանց հասանելիություն: | Citation/Abstract Full Text - PDF |
| Ցուցիչներ: |
Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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| 022 | |a 1539-3100 | ||
| 022 | |a 1539-3119 | ||
| 024 | 7 | |a 10.4018/IJDET.2017010104 |2 doi | |
| 035 | |a 2931899255 | ||
| 045 | 2 | |b d20170101 |b d20170331 | |
| 084 | |a 66571 |2 nlm | ||
| 100 | 1 | |a Liu, Ming |u School of Computer and Information Science, Southwest University, Chongqing, China | |
| 245 | 1 | |a Automated Scoring of Chinese Engineering Students' English Essays | |
| 260 | |b IGI Global |c 2017 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Particle swarm optimization | ||
| 653 | |a Regression | ||
| 653 | |a Algorithms | ||
| 653 | |a Swarm intelligence | ||
| 653 | |a Machine learning | ||
| 653 | |a Support vector machines | ||
| 653 | |a Kernel functions | ||
| 653 | |a Engineering education | ||
| 653 | |a Students | ||
| 653 | |a Language | ||
| 653 | |a Regression analysis | ||
| 653 | |a Writing | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Automation | ||
| 653 | |a Distance learning | ||
| 653 | |a Text analysis | ||
| 653 | |a Essays | ||
| 653 | |a Engineering | ||
| 653 | |a Linguistics | ||
| 653 | |a English teachers | ||
| 653 | |a Intelligence | ||
| 653 | |a Education | ||
| 653 | |a Semantics | ||
| 653 | |a Information science | ||
| 653 | |a Computer science | ||
| 653 | |a Function words | ||
| 653 | |a Optimization | ||
| 653 | |a Content words | ||
| 653 | |a Spelling | ||
| 653 | |a Scores | ||
| 653 | |a Multiple Regression Analysis | ||
| 653 | |a Learning Problems | ||
| 653 | |a Cognitive Processes | ||
| 653 | |a Measurement Techniques | ||
| 653 | |a College English | ||
| 653 | |a Computer Uses in Education | ||
| 653 | |a Language Processing | ||
| 653 | |a English | ||
| 653 | |a Grammar | ||
| 653 | |a Influence of Technology | ||
| 653 | |a Distance Education | ||
| 653 | |a Educational Technology | ||
| 653 | |a English (Second Language) | ||
| 653 | |a Periodicals | ||
| 653 | |a Native Language | ||
| 653 | |a Instructional Materials | ||
| 653 | |a English Learners | ||
| 653 | |a Computational Linguistics | ||
| 653 | |a College Science | ||
| 700 | 1 | |a Wang, Yuqi |u School of Computer and Information Science, Southwest University, Chongqing, China | |
| 700 | 1 | |a Xu, Weiwei |u College of International Studies, Southwest University, Chongqing, China | |
| 700 | 1 | |a Liu, Li |u School of Software Engineering, Chongqing University, Chongqing, China | |
| 773 | 0 | |t International Journal of Distance Education Technologies |g vol. 15, no. 1 (2017), p. 52 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2931899255/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2931899255/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |