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Diagnosis by Rough Set and Information Theory in Reinforcing the Competencies of the Collegiate

러프집합과 정보이론을 이용한 대학생역량강화 진단

  • 박인규 (중부대학교 컴퓨터학과)
  • Received : 2014.05.16
  • Accepted : 2014.08.20
  • Published : 2014.08.28

Abstract

This paper presents the core competencies diagnosis system which targeted our collegiate students in an attempt to induce the core competencies for reinforcing the learning and employment capabilities. Because these days data give rise to a high level of redundancy and dimensionality with time complexity, they are more likely to have spurious relationships, and even the weakest relationships will be highly significant by any statistical test. So as to address the measurement of uncertainties from the classification of categorical data and the implementation of its analytic system, an uncertainty measure of rough entropy and information entropy is defined so that similar behaviors analysis is carried out and the clustering ability is demonstrated in the comparison with the statistical approach. Because the acquired and necessary competencies of the collegiate is deduced by way of the results of the diagnosis, i.e. common core competencies and major core competencies, they facilitate not only the collegiate life and the employment capability reinforcement but also the revitalization of employment and the adjustment to college life.

본 논문은 대학생을 대상으로 학생들의 학업 및 취업경쟁력 강화에 필요한 핵심 역량의 도출과 진단에 관한 연구이다. 이러한 데이터의 처리에는 매우 많은 변수로 인한 차원의 증가로 인하여 계산상의 어려움이 수반되어지고 변수의 중복성과 중요도에 있어서 다양한 통계적 관계가 존재한다. 따라서 범주형 데이터의 분류에서 발생하는 애매함이나 불확실성을 처리하기 위하여 러프집합과 정보 엔트로피를 기반으로 불확실성의 척도를 정의하여 학생들의 유사행동을 분석하고, 기존의 통계적인 방법과의 비교우위를 위하여 속성간의 변별력을 비교하였다. 도출된 공통 핵심역량과 전공핵심역량을 이용하여 학생들이 가지고 있는 역량의 정성적인 보유수준과 부족한 역량을 파악할 수 있기 때문에, 대학생활지도와 취업진로지도의 보조자료로 활용이 가능할 뿐만 아니라 대학 적응을 높이고 취업 활성화에 부합될 수 있다고 사료된다.

Keywords

References

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