ABSTRACT

The purpose of this study is to develop a method for learning musical instruments using a music platform with artificial intelligence(machine learning) technology focusing on self-directed learning. For this purpose, machine learning and self-directed learning, concept explanation of online music platform, and chordify program were introduced. A class instruction plan was proposed to select and implement a course consisting of a total of 8 sessions and music learning upper grades of elementary school using an after-school program that allows self-directed learning. The development of artificial intelligence, following the combination of new mobile and digital technologies, is leading to many changes in the individual learning environment by making it easier to acquire network-based information. Self-directed instrumental learning that breaks away from the traditional teaching environment will provide learners with a high sense of achievement in an online learning environment with relative freedom of time and space. Furthermore, the music platform with machine learning will provide customized step-by-step practice to beginning performers and provide an optimized environment for learning instruments through individually selected programs.

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REFERENCES

1. Bae, J. H. (2024). Fully math participates in '2024 Korea glocal future education expo'. Kyungsang Ilbo. Retrived August 21, 2024, from https://www.ksilbo.co.kr/news/article View.html?idxno=1000463
2. Cho, D. Y. (2005). The relationship between self-direction in learning and interpersonal skills within teams. The Journal of Research in Education, 23, 223-242.
3. Choi, H. H. (2024). Platformization and music industry. The Journal of the Korea Contents Association, 241-242.
4. Choi, Y. K., & Chung, M. R. (2014). The effect of training in music drama play on pre-servicee early childhood teachers' self-directed learning ability and self-efficacy. Korean Journal of Research in Music Education, 43(3), 135-156.
5. Han, S. I. (2021). Speech-music discrimination using deep learning. Journal of the Korea Academia-Industrial Cooperation Society, 22(10), 552-557. https://doi.org/10.5762/KAIS.2021.22.10.552 [Crossref]
6. Jeon, E. J. (2020). An analysis of the actual condition of music education in the untact era : Focusing on art high school practical music. The Korean Society of Culture and Convergence, 42(11), 15-35. https://doi.org/10.33645/cnc.2020.11.42.11.15 [Crossref]
7. Jeon, S. J., & Yu, H. H. (2020). Relationship between general characteristics, learning flow, self-directedness and learner satisfaction of medical students in online learning environment. The Journal of the Korea Contents Association, 20(8), 65-74.
8. Jeong, G. O. (2007). Policy background and implications of the introduction of after-school programs (RM 2007-70), Korean Educational Development Institute, 1-12.
9. Jeong, J. E. (2024). Music is also AI...search and recommendations are basic, even composition. The Korea Economic Daily, Hankyung. Retrieved August 10, 2024, from https://www.hankyung.com/article/202402130484i.
10. Jin, S. H. (2021). The effects of using Vlogs for English language learning Korean EFL learners' self-directed learning ability, learning interest, and speak skills. Multimedia-Assisted Language Learning, 24(3), 157-186.
11. Jo, H. R. (2002). A study on the programs to improve self-directed learning abilities by using web materials. Master thesis, Keimyung University.
12. Jo, J. O. (2012). A study on self-directed learning using multimedia for a college general English course. STEM Journal, 13(3), 135-160. https://doi.org/10.16875/stem.2012.13.3.135 [Crossref]
13. Kang, S. H. (2003). The effects of the type of cyber class and self-regulated learning on learning achievement. Journal of Korean Association for Educational Information and Media, 9(4), 209-228.
14. Kim, B. K. (2024). Elementary school teachers' perceptions regarding after-school music education and their suggestions for improvement. The Journal of Elementary Education, 37(2), 79-105. https://doi.org/10.29096/JEE.37.2.04 [Crossref]
15. Kim, E. J. (2021). Exploring teacher's instruction competencies through music class case analysis in the context of COVID-19. Korean Journal of Elementary Education, 32(3), 167-184.
16. Kim, E. J., & Min, K. H. (2023). Exploring the direction of development of music textbooks for music education in the new normal era. Journal of Music Education Science, 57, 181-201. https://doi.org/10.30832/JMES.2023.57.181 [Crossref]
17. Kim, H., & Kim, J. C. (2012). A study on the effects of after-school program participation on children's accademic achievement: Focused on the mediating effects on self-directed learning skills and creativity. Soonchunhyang Journal of Humanities, 31(2), 172-199.
18. Kim, H. K., Lee, H. Y., & Park, J. W. (2019). A study on the automatic extraction of formulation and properties in chemical field patent document by using machine learning technology. Korea Society of Computer Information Summer Conference, 27(2), 277-280.
19. Kim, H. J., Cha, Y. S., & Kim, K. H. (2016). Case analysis on platform business models for IT service planning. Korea Science & Art Forum, 25, 103-118. https://doi.org/10.17548/ksaf.2016.09.25.103 [Crossref]
20. Kim, S. H, Kim, D. S., & Suh, B. W. (2016). Music genre classification using multimodal deep learning. Proceedings of HCI Korea 2016, 389-395. https://doi.org/10.17210/hcik.2016.01.389 [Crossref]
21. Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. New York: Association Press.
22. Ko, J. M., & Park, M. Y. (2024). A study on changes in the music industry by platform type. Journal of Culture Industry, 24(1), 143-155. https://doi.org/10.35174/JKCI.2024.03.24.1.143 [Crossref]
23. Kwon, J. W., & Shin, J. H. (2022). Analysis of music online and offline classes in middle and high schools in seoul in the COVID-19 situation. The Korean Journal of Arts Education, 20(4), 63-80.
24. Lee, J. S. (2012). The influence of military superior's transformational leadership, self-directed learning ability and learning motivation upon adjustment and organizational commitment. Doctoral dissertation, Chungang University.
25. Lee, W. G., & Lee, M. H. (2021). Machine learning data sharing and utilization platform. The Journal of the Korea Contents Association, 279-280.
26. Lim, S. J., & Min, O. K. (2012). Machine learning technology trends for big data processing. Electronics and Telecommunications Trends, 137, 55-63
27. Lim, S. K., Oh, T, E., & Park, K. S. (2022). A study on the difference in consumers' perception of the e-commerce utilization factors according to COVID-19: Focused on fresh food. The e-Business Studies, 23(1), 75-94. https://doi.org/10.20462/tebs.2022.2.23.1.75 [Crossref]
28. Ministry of Education (2022). Music curriculum (Ministry of Education Notice No. 2022-33). Sejong: Author.
29. Min, K. H. (2015). Textbook for self-directed learning of music subjects [Electronic source]. Korea Textbook Research Foundation, Textbook Research, 80, 26-30.
30. Park, H. J. (2020). Trend analysis of Korea papers in the fields of 'artificial intelligence','machine learning' and 'deep learning'. Korea Information Electron Communication Technology, 13(4), 283-292.
31. Park, J. Y. (2008). A study on the development and the effects of self-directed learning model by project method. Doctoral dissertation, Catholic Kwandong University.
32. Park, M. J. (2021). Exploring the predictability of self-directed learning, quality of instruction, and learning flow for the learning outcomes of online courses. CNU Journal of Educational Studies, 41(1), 135-162.
33. Seo, J. H., & Park, J. P. (2024). A study on classification of music genre based on maching learning. Journal of the Korea Academia-Industrial Cooperation Society, 25(6), 63-69. https://doi.org/10.5762/KAIS.2024.25.6.63 [Crossref]
34. Seung, Y. H., Min, K. H., Yang, J. M., & Chung, J. W. (2019). Music education in elementary school (2nd ed.). Seoul: Hakjisa.
35. Song, M. Y. (2014). The effects of self-directed instrumental ensemble classes on improving the leadership of middle school students. Master thesis, Ewha Womans University.
36. Song, J. M. (2012). A study of the effects of creativity and personal responsibility orientation on self-directed learning of adults. Doctoral dissertation, Chungnam National University.
37. Statistics Korea. (2024). Pay attention to the AI platform where AI services begin. 2017 winter. Retrieved August 3, 2024, from https://sti.kostat.go.kr/window/2017b/html/201 7_win_8.html.
38. Tough, A. M. (1979). The adult's learning projects. A fresh approach to theory and practice in adult learning (2nd ed.). Toronto: Ontario Institute for Studies in Education.
39. Yoon, J. M. (2024). KT, establishes additional AI customized teaching and learning platform in 11 offices of education. Newsis. Retrieved July 29, 2024, from https://www.newsis.com/view/NISX20240621_0002781469