Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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A Genetic Algorithm Based Learning Path Optimization for Music Education

유전 알고리즘 기반의 음악 교육 학습 경로 최적화

  • Jung, Woosung (Graduate School of Education, Seoul National University of Education)
  • 정우성 (서울교육대학교 교육전문대학원)
  • Received : 2019.01.15
  • Accepted : 2019.02.20
  • Published : 2019.02.28
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Abstract

For customized education, it is essential to search the learning path for the learner. The genetic algorithm makes it possible to find optimal solutions within a practical time when they are difficult to be obtained with deterministic approaches because of the problem's very large search space. In this research, based on genetic algorithm, the learning paths to learn 200 chords in 27 music sheets were optimized to maximize the learning effect by balancing and minimizing learner's burden and learning size for each step in the learning paths. Although the permutation size of the possible learning path for 27 learning contents is more than $10^{28}$, the optimal solution could be obtained within 20 minutes in average by an implemented tool in this research. Experimental results showed that genetic algorithm can be effectively used to design complex learning path for customized education with various purposes. The proposed method is expected to be applied in other educational domains as well.

맞춤형 교육을 위해 학습자에 맞는 학습 경로를 탐색하는 것은 필수적이다. 유전 알고리즘은 해공간이 매우 커서 결정적 방법으로 해를 구하기 어려울 때 타당한 시간 내에 최적해를 찾게 해준다. 본 연구는 유전 알고리즘을 이용하여 200개 코드를 가진 악보 27개를 대상으로 학습자 부담을 최소화하고 단계별 학습량을 균등하게 분산함으로써 학습 효과를 최대화 할 수 있도록 학습 경로를 최적화하였다. 학습 컨텐츠가 27개만 되어도 학습 경로의 순열 크기는 $10^{28}$을 넘지만, 본 연구에서 구현한 도구로 평균 20분 이내에 최적해를 구할 수 있었다. 실험 결과는 유전 알고리즘이 다양한 목적의 맞춤형 교육을 위한 복잡한 학습 경로 설계에 효과적임을 보여주었다. 제안한 방법은 다른 교육 도메인에도 활용할 수 있을 것으로 기대된다.

Keywords

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Fig. 1. An overall scenario for problem definition

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Fig. 2. Activity diagram for overall approach

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Fig. 3. Layered architecture of the proposed optimizer

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Fig. 4. Database schema for MusicXMLParser

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Fig. 5. A meta-model for learner’s profile

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Fig. 6. Chromosome and fitness function for learning path optimization

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Fig. 7. Genetic algorithm for learning path optimization

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Fig. 8. static analysis of the results

Table 1. Experimental results based on chord rate

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Table 2. Experimental results based on chord count

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