• Korean Journal of Cognitive Science
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• v.26 no.2
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• pp.147-165
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• 2015

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.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.354-359
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• 2022

에세이 자동 평가는 주어진 에세이를 읽고 자동으로 평가하는 작업이다. 본 논문에서는 효과적인 에세이 자동 평가 모델을 위해 Argument Mining 작업을 사용하여 에세이의 논증 구조가 반영된 에세이 표현을 만들고, 에세이의 평가 항목별 표현을 학습하는 방법을 제안한다. 실험을 통해 제안하는 에세이 표현이 사전 학습 언어 모델로 얻은 표현보다 우수함을 입증했으며, 에세이 평가를 위해 평가 항목별로 다른 표현을 학습하는 것이 보다 효과적임을 보였다. 최종 제안 모델의 성능은 QWK 기준으로 0.543에서 0.627까지 향상되어 사람의 평가와 상당히 일치한다.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.526-530
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• 2022

자동 쓰기 평가 연구는 쓰기 답안지를 채점하는데 드는 시간과 비용을 절감할 수 있어, 교육 분야에서 큰 관심을 가지고 있다. 본 연구의 목적은 쓰기 답안지의 문서 구조를 효과적으로 학습하여 평가하고, 문장단위의 피드백을 제공하는데 있다. 그 방법으로는 문장 레벨에서 한국어 Sentence-BERT 모델을 활용하여 각 문장을 임베딩하고, LSTM 어텐션 모델을 활용하여 문서 레벨에서 임베딩 문장을 모델링한다. '한국어 쓰기 텍스트-점수 구간 데이터'를 활용하여 해당 모델의 성능 평가를 진행하였으며, 다양한 KoBERT 기반 모델과 비교 평가를 통해 제안하는 모델의 방법론이 효과적임을 입증하였다.

• Journal of KIISE
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• v.42 no.9
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• pp.1124-1132
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• 2015

In order to train a supervised machine learning algorithm, it is necessary to have non-biased labels and a sufficient amount of training data. However, it is difficult to collect the required non-biased labels and a sufficient amount of training data to develop an automatic English Composition scoring system. In addition, an English writing assessment is carried out using a multi-faceted evaluation of the overall level of the answer. Therefore, it is difficult to choose an appropriate machine learning algorithm for such work. In this paper, we show that it is possible to alleviate these problems through ensemble learning. The results of the experiment indicate that the ensemble technique exhibited an overall performance that was better than that of other algorithms.

• The KIPS Transactions:PartA
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• v.12A no.6 s.96
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• pp.539-546
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• 2005

Recently, as the number of Internet users are growing explosively, e-learning has been applied spread, as well as remote evaluation of intellectual capacity However, only the multiple choice and/or the objective tests have been applied to the e-learning, because of difficulty of natural language processing. For the intelligent marking of short-essay typed answer papers with rapidness and fairness, this work utilize heterogenous linguistic knowledges. Firstly, we construct the semantic kernel from un tagged corpus. Then the answer papers of students and instructors are transformed into the vector form. Finally, we evaluate the similarity between the papers by using the semantic kernel and decide whether the answer paper is correct or not, based on the similarity values. For the construction of the semantic kernel, we used latent semantic analysis based on the vector space model. Further we try to reduce the problem of information shortage, by integrating Korean Word Net. For the construction of the semantic kernel we collected 38,727 newspaper articles and extracted 75,175 indexed terms. In the experiment, about 0.894 correlation coefficient value, between the marking results from this system and the human instructors, was acquired.

• The Journal of Korean Association of Computer Education
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• v.17 no.4
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• pp.45-57
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• 2014

Many studies on algorithm judging system which verifies the correctness and the time efficiency of your program have been underway recently, most of which are on an online judging system focused on programming contests. However this study is mainly about development and application of the judging system based on client-server. Especially, we designed to promote metacognition and motivation which are emphasized in CRESST model, and implemented the total system that consists of the problem, data set, validation program, and user service environments. We applied our system to elementary, middle, and high school students, and We noticed a significant difference of average score between the experimental and control group in posttest and concluded that the teaching method using our system gave the bigger positive effects on programming learning.