Browse > Article
http://dx.doi.org/10.15434/kssh.2020.33.3.184

Trend Analysis of School Health Research using Latent Semantic Analysis  

Shin, Seon-Hi (The Graduate School of Educational Policy and Administration, Korean National University of Education)
Park, Youn-Ju (The Graduate School of Educational Policy and Administration, Korean National University of Education)
Publication Information
Journal of the Korean Society of School Health / v.33, no.3, 2020 , pp. 184-193 More about this Journal
Abstract
Purpose: This study was designed to investigate the trends in school health research in Korea using probabilistic latent semantic analysis. The study longitudinally analyzed the abstracts of the papers published in 「The Journal of the Korean Society of School Health」 over the recent 17 years, which is between 2004 and August 2020. By classifying all the papers according to the topics identified through the analysis, it was possible to see how the distribution of the topics has changed over years. Based on the results, implications for school health research and educational uses of latent semantic analysis were suggested. Methods: This study investigated the research trends by longitudinally analyzing journal abstracts using latent dirichlet allocation (LDA), a type of LSA. The abstracts in 「The Journal of the Korean Society of School Health」 published from 2004 to August 2020 were used for the analysis. Results: A total of 34 latent topics were identified by LDA. Six topics, which were「Adolescent depression and suicide prevention」, 「Students' knowledge, attitudes, & behaviors」, 「Effective self-esteem program through depression interventions」, 「Factors of students' stress」, 「Intervention program to prevent adolescent risky behaviors」, and 「Sex education curriculum, and teacher」were most frequently covered by the journal. Each of them was dealt with in at least 20 papers. The topics related to 「Intervention program to prevent adolescent risky behaviors」, 「Effective self-esteem program through depression interventions」, and 「Preventive vaccination and factors of effective vaccination」 appeared repeatedly over the most recent 5 years. Conclusion: This study introduced an AI-powered analysis method that enables data-centered objective text analysis without human intervention. Based on the results, implications for school health research were presented, and various uses of latent semantic analysis (LSA) in educational research were suggested.
Keywords
Trend analysis; Latent semantic analysis; School health research; Latent dirichlet allocation (LDA);
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Robert Donmoyer, Michael Imber, James Joseph Scheurich. The knowledge base in educational administration: multiple perspectives. Albany: State University of New York Press; 1995. p. 1-31.
2 Bae SH, Jang HY, Oh SH, Jang CS, Lee TH. Vocational education research in Korea: a trend analysis of the Journal of Vocational Education Research. Journal of Vocational Education. 2013;32(2):45-71.
3 Jung JS, Kim JS. The analysis of the research trends related to school health in Korea. Journal of Korean Society of School Health. 2004;17(1):85-95.
4 Kwon SJ. Anaysis of research trends on school health. Journal of Korean Academy of Community Health Nursing. 2008;19 (1):101-111.
5 Jo HI, Kim JW, Lee BG. A study on research trends of blockchain using LDA topic modeling: focusing on United States, China, and South Korea. Journal of Digital Contents Society. 2019;20(7):1453-1460. https://doi.org/10.9728/dcs.2019.20.7.1453   DOI
6 Hong YH. Topic analysis of software education policy: focused on Busan regional newspapers. Journal of the Korean Official Statistics. 2019;24(2):52-77. https://doi.org/10.22886/JKOS.2019.24.2.52   DOI
7 Zhai C, Massung S. Text data management and analysis: a practical introduction to information retrieval and text mining. San Rafael, California: Association for Computing Machinery and Morgan & Claypool Publishers; 2016. p. 329-388.
8 Kim JE, Pack SG. Analysis of issues on the college and university structural reform evaluation using text big data analytics. Asia-Pacific Education Review. 2016;17(3):409-436. https://doi.org/10.15753/aje.2016.09.17.3.409   DOI
9 Blei DM, Lafferty JD. A correlated topic model of science. The Annals of Applied Statistics. 2007;8(1):17-35. https://doi.org/10.1214/07-aoas114   DOI
10 Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. Journal of Machine Learning Research. 2003;3:993-1022.
11 Grun B, Hornik K. Topicmodels: an R package for fitting topic models. Journal of Statistical Software. 2011;40(13):1-30. https://doi.org/10.18637/jss.v040.i13   DOI
12 Akaike H. Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F, editors. Second international symposium on information theory. Budapest, Hungary: Akademiai Kiado; 1973. p. 267-281.
13 Schwarz G. Estimating the dimension of a model. The Annals of Statistics. 1978;6(2):461-464. https://doi.org/10.1214/aos/1176344136   DOI
14 Tein JY, Coxe S, Cham H. Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling. 2012;20(4):640-657. https://doi.org/10.1080/10705511.2013.824781   DOI
15 Cao J, Xia T, Li J, Zhang Y, Tang S. A density-based method for adaptive LDA model selection. Neurocomputing. 2009;72:1775-1781. https://doi.org/10.1016/j.neucom.2008.06.011   DOI
16 Shin S, Seo D, Park M. Effectiveness of Latent Class Analysis (LCA) techniques to handle count data with excessive zeros in topic analysis. Poster session presented at: The 32nd florida artificial intelligence research society; 2019 May 19-22; Florida, USA.
17 Ponweiser M. Latent dirichlet allocation in R [diploma thesis]. Iowa: Institute for Statistics and Mathematics; 2012. p. 1-138.
18 Wallach HM, Murray I, Salakhutdinov R, Mimno D. Evaluation methods for topic models. Pittsburgh: In Proceedings of the 26th Annual International Conference on Machine Learning; 2009. p. 1105-1112.
19 Chang J, Blei DM. Relational topic models for document networks. Proceedings of the 12th International Conference on Artificial Intelligence and Statistics. 2009;81-88.
20 Griffiths TL, Steyvers M. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(suppl 1):5228-5235. https://doi.org/10.1073/pnas.0307752101   DOI
21 Collins LM, Lanza ST. Latent class and latent transition analysis. Hoboken, NJ: John Wiley & Sons; 2010. p. 77-110.
22 Chang CG. Development of school health indicator system for the health promotion of school children. Journal of the Korean Society of School Health. 2012;25:204-213.
23 Park YJ. Looking for a new perspective on school health promotion. Journal of the Korean Society of School Health. 2018;31:157-166. https://doi.org/10.15434/kssh.2018.31.3.157   DOI