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Semi-Automatic Scoring for Short Korean Free-Text Responses Using Semi-Supervised Learning  

Cheon, Min-Ah (Korea Maritime and Ocean University)
Seo, Hyeong-Won (Korea Maritime and Ocean University)
Kim, Jae-Hoon (Korea Maritime and Ocean University)
Noh, Eun-Hee (Korea Institute for Curriculum and Evaluation)
Sung, Kyung-Hee (Korea Institute for Curriculum and Evaluation)
Lim, EunYoung (Korea Institute for Curriculum and Evaluation)
Publication Information
Korean Journal of Cognitive Science / v.26, no.2, 2015 , pp. 147-165 More about this Journal
Abstract
Through short-answer questions, we can reflect the depth of students' understanding and higher-order thinking skills. Scoring for short-answer questions may take long time and may be an issue on consistency of grading. To alleviate such the suffering, automated scoring systems are widely used in Europe and America, but are in the initial stage in research in Korea. In this paper, we propose a semi-automatic scoring system for short Korean free-text responses using semi-supervised learning. First of all, based on the similarity score between students' answers and model answers, the proposed system grades students' answers and the scored answers with high reliability have been included in the model answers through the thorough test. This process repeats until all answers are scored. The proposed system is used experimentally in Korean and social studies in Nationwide Scholastic Achievement Test. We have confirmed that the processing time and the consistency of grades are promisingly improved. Using the system, various assessment methods have got to be developed and comparative studies need to be performed before applying to school fields.
Keywords
Automated scoring; Machine learning; Semi-Supervised learning;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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