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http://dx.doi.org/10.14352/jkaie.2021.25.4.633

Analysis of Educational Issues through Topic Modeling of National Petitions Text  

Shim, Jaekwoun (Korea University Center for Gifted Education)
Publication Information
Journal of The Korean Association of Information Education / v.25, no.4, 2021 , pp. 633-640 More about this Journal
Abstract
Education related issues are social problems in which various groups and situations are intricately linked to each other. It is difficult to find issues by analyzing social phenomena related to education. Korean based text analysis can be analyzed in a quantitative. With the development of text analysis techniques, research results have been recently achieved, and it can be fully utilized to derive educational issues from text data in Korean. In this study, petition articles in the field of childcare/education were collected on the online-board of the Blue House National Petition website, and text analysis was used to derive issues in the education world. The analysis derived 6 topics through Latent Dirichlet Allocation(LDA) among topic modeling techniques. The association rules of major keywords were analyzed and visualized as graphs. In addition to deriving educational issues through the existing questionnaire, it can provide implications for future research directions and policies in that issues can be sufficiently discovered through text-based analysis methods.
Keywords
National Petitions; Topic Modeling; Latent Dirichlet Allocation; Association Rule; Education Issue;
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  • Reference
1 D. Newman, J. H. Lau, K. Grieser, and T.Baldwin (2010). Automatic Evaluation of Topic Coherence. Human Language Technologies. The 2010 Annual Conference of the North American Chapter of the ACL, 100-108.
2 T. K. Son, and S. H, Hwang (2020). Analysis of the Research Trends of Domestic Elementary Mathematics Education Using Topic Modeling. Journal of Elementary Mathematics Education in Korea, 25(1), 61-80.   DOI
3 S. J. Kang and Y. J. Shon (2020). Phenomenon of Early Childhood Private Education through Topic Modeling Analysis: Focusing on Domestic Newspaper Articles and Blogs. Journal of Future Early Childhood Education, 27(1), 177-199.   DOI
4 S. Y. Kim (2020). Analysis of Research Trends in Journal of the Korean Society for Industrial and Applied Mathematics Using Topic Modeling and Implications for Industrial Mathematics Education. Secondary Education Research, 68(2), 267-293.
5 T. K. Kim, H. R. Choi and H. C. Lee (2016). A Study on the Research Trends in Fintech using Topic Modeling. Journal of the Korea Academia-Industrial cooperation Society, 17(11), 670-681.   DOI
6 D.W. Yoon and H.J. Choe (2019). Analysis of the Core Concepts of Middle School Informatics Textbook Using Big Data Analysis Techniques. Journal of Creative Information Culture, 5(2), 157-164.   DOI
7 W.J. Choi, J.W. Seol, H.S. Jeong, and H.M. Yoon (2018). Comparison and Analysis of Subject Classification for Domestic Research Data. The Journal of the Korea Contents Association, 18(8), 178-186.   DOI
8 Y. Kim, N.K. Kim and S.J. Lee (2021). A Study on the Influence of Communication Characteristics of One-Person Media on Intention to Contents Acceptance: Focusing on the Mediating Effect of Parasocial Interaction. The Korean Journal of Advertising, 32(2), 163-188.   DOI
9 Yon, B.N (2020). A Topic Modeling Analysis on the Policy Issues of Meister High School. Journal of Vocational Education & Training, 23(1), 39-67.   DOI
10 C. W. Woo and J. Y. Lee (2020). Investigation of Research Topic and Trends of National ICT Research-Development Using the LDA Model. Journal of the Korea Convergence Society, 11(7), 9-18.   DOI
11 David M. Blei, Andrew Y. Ng and Michael I. Jordanm (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
12 J. W. Min, and J. K. Shim (2020). A Study on Analysis of National Petition Datafor Deriving Current Issues in Education. Journal of Creative Information Culture, 6(2), 57-64.   DOI
13 Jonathan Chang, Jordan Boyd-Graber, Sean Gerrish, Chong Wang and David M. Blei (2009). Reading Tea Leaves: How Humans Interpret Topic Models. Advances in Neural Information Processing Systems, 22, 288-296.
14 Blue House National Petition Homepage [Internet]. https://www1.president.go.kr/petitions/FAQ
15 C.D. Kang (2008). A Social Historical Study on the Korean Paranoid Educational Fervor and Status Desire. The Korea Educational Review, 14(2), 5-32.
16 M. Steyvers, and T. Griffiths (2007). Probabilistic topic models, Handbook of latent semantic analysis, Lawrence Erlbaum Associates Publishers.