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A Genetic Algorithm Based Learning Path Optimization for Music Education

유전 알고리즘 기반의 음악 교육 학습 경로 최적화

Jung, Woosung
정우성

  • Received : 2019.01.15
  • Accepted : 2019.02.20
  • Published : 2019.02.28

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.

Keywords

Music education;MusicXML;Learning path;Genetic algorithm;Optimization

Acknowledgement

Supported by : Seoul National University of Education