- AUTHOR :
- Mi Sook Kim, Ji Young Lee
- INFORMATION:
- page. 1~21 / 2023 Vol.52 No.2
ABSTRACT
The purpose of this study is to analyze the characteristics of topics by period and identify research trends by using text mining techniques in papers published in ‘Korea Journal of Research in Music Education (KJRME)’, a representative academic journal for music education in Korea. Using text network analysis, abstracts of a total of 621 published papers were analyzed using the Python program. conclusions are asfollowing. First, the tendency of domestic music education is student-centered. Second, according to the frequency analysis by period in KJRME, there were many basic and general studies at the beginning of the study. Third, as a result of topic modeling of KJRME, domestic music education has been studied with various themes.
Keyword :
REFERENCES
- Blei, D. M., Ng, A, Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Jounal of Machine Learning Research, 3, 993-1022.
- Chang, J. N., & Na, J. Y. (2022a). How the journal of the Korean association for science education(JKASE) changed for the past 44 years?: Topic modeling analysis using latent dirichlet allocation. Journal of the Korean Association for Science Education, 42(2), 185-200.
- Chang, J. N., & Na, J. Y. (2022b). An examination of the topics and changes in the research papers published in the journal of Korean elementary science education using latent dirichlet allocation for the topic modeling analysis. Journal of Korean Elementary Science Education, 41(2), 356-372.
- Choi, E. S. (2000). Research tasks and prospects of Korean music curriculum education in the 21st century. Korean Music Education Society The 2nd Quater Research Seminar, 1-20.
- Choi, J. H. (2011). Research methodology of music education in Korea. Journal of Music Education Science, 12, 205-220.
- Hwang, E. Y. (2020). Analysis on topic modeling and trend of 'Korean journal of music therapy' using text mining(1999~2019). Korean Journal of Music Therapy, 22(2), 29-47.
- Im, S. R., Paik, S. H., Min, K. H., & Song, Y. Y. (2020). An analysis of domestic research trends of the music-centered convergence education. Journal of Music Education Science, 45, 107-126. https://doi.org/10.30832/JMES.2020.45.107
[Crossref]
- Jung, J. E., & Seog, M. J. (2014). The research trends of music education in Korea by the analysis of 'Korean journal of research in music education'. Korean Journal of Research in Music Education, 43(1), 165-188.
- Kim, H. K., & Hwang, W. Y. (2020). Proposal for improving data processing performance using Python. Journal of Korea Institute of Information, Electronics, and Communication Technology, 13(4), 306-311.
- Kim, H. M. (2015). Analysis of research trends of South Korean music education through semantic network analysis: Focusing on Korean journal of research in music education. Korean Journal of Research in Music Education, 44(4), 49-68.
- Kim, J. H. (2022). Research trend analysis of pre-service music teachers in Korea using text mining. Journal of Music Education Science, 52, 79-95. https://doi.org/10.30832/JMES.2022.52.79
[Crossref]
- Kim, M. S. (2001). The study on the trends of research in JRME. Korean Journal of Research in Music Education, 20(1), 1-19.
- Kim, W. J. (2018). A study on analysis of the research trend and the knowledge structure of music education by analyzing keyword network. Research in Music Pedagogy, 19(1), 1-30.
- Kwag, H. G., & Kwag, M. S. (2017). A study on the research trend of music education in Korea with special reference to 'Korean journal of research in music education': Focusing on key words selected by the author. Korean Journal of Research in Music Education, 46(1), 1-21. https://doi.org/10.30775/KMES.46.1.01
[Crossref]
- Lee, W. M., & Kwon, G. M. (2019). An analysis of the research topics in the elderly sports using topic modeling: Focusing on the humanities and social sciences. The Korean Journal of Physical Education, 58(6), 253-262. https://doi.org/10.23949/kjpe.2019.11.58.6.20
[Crossref]
- Oh, J. H., & Shin, H. K. (2017). Pre-service music teachers' self-assessment of reaching ability using text network analysis. Korean Journal of Research in Music Education, 46(4), 47-75. https://doi.org/10.30775/KMES.46.4.03
[Crossref]
- Park, K. B. (2011). An analysis and survey of Korean research on music education: Focusing on the research published in Korean journals since 1980. Korean Journal of Arts Education, 9(2), 57-76.
- Park, Y. J. (2014). The study of 'Classical music' as reflected in bigdata: The concept, musicians, and opinions. Journal of Music Education Science, 19, 127-144.
- Seog, M. J. (2004). The alternative approach of music education inquiry in Korea through current research trends and issues in music education. Korean Journal of Research in Music Education, 27, 91-128.
- Shin, H. K. (2013). Trends and issues of qualitative research in music education in the United States. Korean Journal of Research in Music Education, 42(2), 91-117.
- Shin, H. K., & Oh, J. H. (2019). A comparative study on the research trends of music education in Korea and the US through text network analysis: Focusing on KJRME and JRME. Korean Journal of Research in Music Education, 23(3), 185-200.
- You, J. M. (2020). A study on STEAM education policy using big data. Ed.D diss., Ewha Womans University.
- Yu, Y. L. (2017). Analysis of media coverage on 2015 revised curriculum policy using big data analysis. Ed.D diss., Seoul National University.
- Won, Y. S. (2006). A study on the latest tendency in research of Korean music education. Kukakgyouk, 24, 7-30.
- Won, Y. K., & Kim, Y. W. (2021). Analysis of research trends in Korean english education journals using topic modeling. The Journal of the Korea Contents Association, 21(4), 50-59.
- Won, Y. S. (2006). A study on the latest tendency in research of Korean music education. Kukakgyouk, 24, 7-30.
- Won, Y. K., & Kim, Y. W. (2021). Analysis of research trends in Korean english education journals using topic modeling. The Journal of the Korea Contents Association, 21(4), 50-59.