Authors:
Eun-Seo Jang
1
;
Seung-Shik Kang
1
;
Eun-Hee Noh
2
;
Myung-Hwa Kim
2
;
Kyung-Hee Sung
2
and
Tae-Je Seong
2
Affiliations:
1
Kookmin University, Korea, Republic of
;
2
Korea Institute for Curriculum and Evaluation, Korea, Republic of
Keyword(s):
Automatic Scoring, Short-answer Questions, Token-based Answer Template, Morphological Analysis, Natural Language Processing, JSON Format.
Related
Ontology
Subjects/Areas/Topics:
Assessment Software Tools
;
Computer-Aided Assessment
;
Computer-Supported Education
;
Learning/Teaching Methodologies and Assessment
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.