• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.147-148
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• 2015

대학의 컴퓨터 관련 학과에서 데이터베이스에 대한 SQL(DML)을 학생들에게 숙지시키기 위한 시험(정기 시험, 퀴즈)은 필수이며, 이에 대한 채점을 수작업을 하고 있어 교수들에게 채점의 부담으로 작용하고 있다. 본 논문은 이런 부담을 덜기 위한 SQL 채점 자동화 시스템을 개발함으로써 학생들에게 채점의 부담 없이 시험을 통해 충분한 시험을 실시하도록 하는 시스템을 개발하였다. 대학에서 SQL은 SELECT, UPDATE, INSERT, DELETE 4개의 데이터 조작어로 구분되며, 기본적인 구문의 시험이 대부분이다, 이에 대한 채점 구문분석을 위한 파싱트리(parsing tree)을 만들어 자동 채점 시스템을 구현하였다.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.13-21
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• 2011

It is not easy to develop the system as the subjective-type evaluation has the difficulty in natural language processing. This thesis designs and implements the automatic evaluation system with natural language processing technique. To solve the degradation of general evaluation system, we define the question type and improve the performance of evaluation through the adaptive process for each question type. To evaluate the system, we analyze the correlation between human evaluation and term-based evaluation, and between human evaluation and this system evaluation. We got the better result than term-based evaluation. It needs to expand the question type and improve the adaptive processing technique for each type.

• Annual Conference on Human and Language Technology
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• 2012.10a
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• pp.138-141
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• 2012

한국어 서답형 문항의 자동채점 프로그램을 개발하기 위해서는 모범답안, 오답, 부분점수 부여를 위한 세부적인 내용을 채점 기준표로 기술해야 한다. 자동채점에 필요한 구체적인 사항들을 기술하기 위하여 XML 형식으로 정답 템플릿을 정의하였다. 채점에 필요한 내용을 단위 개념으로 정의하고 이를 컴퓨터가 엑세스 가능한 형태의 정답 템플릿을 설계하였다. 정답 템플릿 형식에 맞게 편리하게 템플릿을 작성할 수 있는 작성 도구를 이용하여 학업 성취도평가 각 문항에 대한 채점 기준표를 정답 템플릿으로 작성하여 채점기준표를 작성하는 실험을 수행하였다.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.173-180
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• 2009

The purpose of developing an automated scoring system for English composition is to score English writing tests and to give diagnostic feedback to the test-takers without human's efforts. The system developed through our research detects grammatical errors of a single sentence on morphological, syntactic and semantic stages, respectively, and those errors are calculated into the final score. The error detecting stages are independent from one another, which causes duplicating the identical errors with different labels at different stages. These duplicated errors become a hindering factor to calculating an accurate score. This paper presents a solution to detecting the duplicated errors and improving an accuracy in calculating the final score by eliminating one of the errors.

• Journal of KIISE
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• v.41 no.11
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• pp.911-920
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• 2014

In this paper, we introduce an automatic scoring system for middle-school level writing test based on using machine learning techniques. We discuss overall process and features for building an automatic English writing scoring system. A "concept answer" which represents an abstract meaning of text is newly introduced in order to evaluate the elaboration of a student's answer. In this work, multiple machine learning algorithms are adopted for scoring English writings. We suggest a decision process "optimal combination" which optimally combines multiple outputs of machine learning algorithms and generates a final single output in order to improve the performance of the automatic scoring. By experiments with actual test data, we evaluate the performance of overall automated English writing scoring system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.117-118
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• 2014

정보통신기술, 멀티미디어 기술 및 관련 소프트웨어 등을 이용하여 형성된 가상의 공간(Cyber-Space)에서 교수자와 학습자 간의 수업이 이뤄지는 사이버대학에서 학생들의 성적관리를 위한 원격시험 주관식 시험문제 자동채점 시스템을 연구하였다.

• Journal of The Korean Association For Science Education
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• v.40 no.3
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• pp.321-336
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• 2020

We explore automated scoring models of scientific argumentation. We consider how a new analytical approach using a machine learning technique may enhance the understanding of spoken argumentation in the classroom. We sampled 2,605 utterances that occurred during a high school student's science class on molecular structure and classified the utterances into five argumentative elements. Next, we performed Text Preprocessing for the classified utterances. As machine learning techniques, we applied support vector machines, decision tree, random forest, and artificial neural network. For enhancing the identification of rebuttal elements, we used a heuristic feature-engineering method that applies experts' classification of morphemes of scientific argumentation.

• Korean Journal of Cognitive Science
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• v.19 no.3
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• pp.223-255
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• 2008

In this paper, we introduce an automatic system of scoring English essays. The system is comprised of three main components: a spelling checker, a grammar checker and a lexical cohesion checker. We have used such resources as WordNet, Link Grammar/parser and Roget's thesaurus for these components. The usefulness of an automatic scoring system depends on its reliability. To measure reliability, we compared the results of automatic scoring with those of manual scoring, on the basis of the Kappa statistics and the Multi-facet Rasch Model. The statistical data obtained from the comparison showed that the scoring system is as reliable as professional human graders. This system deals with textual units rather than sentential units and checks not only formal properties of a text but also its contents.

• The Journal of Korean Association of Computer Education
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• v.21 no.6
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• pp.83-92
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• 2018

The subjective question is appropriate for evaluation of deep thinking, but it is not easy to score. Since, regardless of same scoring criterion, the graders are able to produce different scores, we need the objective automatic evaluation system. However, the system has the problem of Korean analysis and comparison. This paper suggests the Korean syntactic analysis and subjective grading system using the syntactic-semantic tree comparator. This system is the hybrid grading system of word based and syntactic-semantic tree based grading. This system grades the answers on the subjective question using the syntactic-semantic comparator. This proposed system has the good result. This system will be utilized in Korean syntactic-semantic analysis, subjective question grading, and document classification.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.267-274
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• 2014

Even though computer based college lecture management system was developed long ago and has been used ever since, developing perfect lecture management is not simple. The main objective of this system is to develop appropriate online lecture supportive management program suitable for college lectures. This system [ADLEMS] mainly focuses on the management of college database lectures, exams, and grades. This system supports management and grading of attendance, reports, quizzes, mid-term, and final exams. Exam management categorizes into multiple choice questions, essay questions, short answer questions, and SQL. Especially for SQL, division analysis was applied when developing grading system for more effective grade management. For essay questions and short answer questions, manual [hand] grading method was used. Every student can verify the grading process in person to alleviate the problems occurring during the grading process.