Abstract
In the present study, to derive future mid/long-term development directions and agendas according to the outcomes of South Korean creativity education policies that have been steadily implemented thus far, opinion mining analyses were conducted utilizing educational data. With regard to analysis methods, creativity education related unstructured data were collected, linkage analysis based higher education policy keywords were extracted, and opinion mining analyses were conducted through the extracted keywords. From the analyzed results, we derived educational systems that can be very important for future development of creativity education and performance factors through the positive and negative data on the educational policies that are currently being implemented. The outcomes of the present study will be a solution that can be utilized hereafter in preparing direction points according to domestic and foreign educational policies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sun, Y., Jia, K.: Research of word sense disambiguation based on mining association rules. In: Third International Symposium on Intelligent Information Technology Application workshops, 21–22 November, NanChang, China, pp. 86–88 (2009). Sklar, B.: Digital Communications, p. 187. Prentice Hall (1998)
Wu, X., Zhu, X., Wu, G.-Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)
Tang, C., Liu, C.: Method of Chinese grammar rules automatically access based on association rules. In: Proceedings of the. Computer Science and Computational Technology, ISCSCT, Shanghai, 20–22 December 2008, vol. 1, pp. 265–268 (2008)
Khan, I.A., Choi, J.T.: An application of educational data mining (EDM) technique for scholarship prediction. Int. J. Softw. Eng. Appl. 8(12), 31–42 (2014)
Xu, Y., Li, Y., Shaw, G.: Reliable representations for association rules. Data Knowl. Eng. 70, 555–575 (2011)
Seo, J.H.: Design of opinion sensitivity dictionary model for big data management (2015)
Denecke, K.: Using SentiWordNet for multilingual sentiment analysis. In: IEEE 24th International Conference on Data Engineering Workshop 2008, Cancun, Mexico, pp. 507–512 (2008)
Stonebraker, M.: SQL databases v. NoSQL databases. Commun. ACM 53(4), 10–11 (2010)
Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1D1A1B03029292).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Seo, JH., Cho, E., Joo, KH. (2018). Analysis of Agenda Prediction According to Big Data Based Creative Education Performance Factors. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_209
Download citation
DOI: https://doi.org/10.1007/978-981-10-7605-3_209
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7604-6
Online ISBN: 978-981-10-7605-3
eBook Packages: EngineeringEngineering (R0)