Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Automatic Classification of Learning Objects Using Case-based Cohesion for Learning Management System

학습관리시스템을 위한 사례 기반 응집도를 이용한 학습객체 자동 분류

  • 김형일 (나사렛대학교 멀티미디어학과) ;
  • 윤현님 (한국폴리텍대학 안성여자캠퍼스 디지털정보과)
  • Received : 2012.11.07
  • Accepted : 2012.11.13
  • Published : 2012.12.31
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Abstract

In this paper, a method for automatic classification of learning objects is proposed for effective management and reuse of learning contents. Proposed method will create cohesion of learning objects using cases of learning objects and perform automatic classification of learning objects by measuring their relationship based on cohesion. Application of proposed method to learning management system has the advantage of reducing the costs for developing learning contents. Simulation has shown the average accuracy of 28.20% with probability-based method and 56.38% with cohesion-based method. Simulation has proved that the method proposed in this paper is effective in automatic classification of learning objects.

본 논문에서는 학습 콘텐츠의 효과적인 관리와 재사용을 위한 학습객체 자동 분류 기법을 제안한다. 제안한 기법은 학습객체들의 발생 사례를 이용하여 학습객체들의 응집도를 생성하고, 응집도를 기반으로 학습객체들의 연관성을 측정하여 학습객체들의 자동 분류를 수행한다. 제안한 기법을 학습관리시스템에 적용하면 학습 콘텐츠의 개발 비용을 절감시킬 수 있는 장점이 있다. 시뮬레이션에서 확률 기반 기법의 평균 정확도는 28.20%로 나타났고, 응집도 기반 기법의 평균 정확도는 56.38%로 나타났다. 시뮬레이션을 통해 본 논문에서 제안한 기법이 학습객체 자동 분류에 효과적이라는 것을 확인하였다.

Keywords

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