KASS: Korean Automatic Scoring System for Short-answer Questions
Year: 2014
Type: conference-paper
Abstract: The scoring of short-answer questions in a national-wide achievement test to public school students needs a lot of human efforts and financial expenses. Since we know that natural language processing technology can be applied to replace the manual scoring process by automatic scoring software, many researchers tried to build an automatic scoring system like c-rater and e-rater in English. In this paper, we explored a Korean automatic scoring system for short and free-text responses. NLP techniques like morphological analysis are used to build a token-based scoring template for increasing the coverage of the automatic scoring process. We performed an experiment to measure the efficiency of the automatic scoring system and it covered about 90 to 95% of the student responses with an agreement rate 95% to the manual scoring.
Authors: Eun-Seo Jang, Seung-Shik Kang, Eun-Hee Noh, Myung-Hwa Kim, Kyung-Hee Sung, Tae-Je Seong
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W2397537809