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Big Data Analysis in School Adjustment Factors using Data Mining

Ko, Sujeong

  • Received : 2019.01.20
  • Accepted : 2019.01.31
  • Published : 2019.03.31

Abstract

Data mining technology is applied to various fields because it is a technique for analyzing vast amount of data and finding useful information. In this paper, we propose a big data analysis method that uses Apriori algorithm, which is a data mining technique, to find the related factors that have negative and positive influences on school adjustment. Among Korea Child and Youth Panel Survey(KCYPS), data related to adjustment to school life and data showing parental inclinations were extracted from the data of fourth grade elementary school students, first year middle school students, and high school freshman students, respectively and we have mapped the useful association rules among them. As a result, the factors affecting school adjustment were different according to the timing of the growth process, we were able to find interesting rules by looking for connections between rules. On the other hand, the factors that positively influenced school adjustment were not significantly different from each other, and overall, they were associated with positive variables.

Keywords

Apriori algorithm;Big data analysis;Data mining;School adjustment factors

References

  1. J. R. Finkel, T. Grenager, and C. Manning. "Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling," in Proc. of the 43nd Annual Meeting of the Association for Computational Linguistics, 2005. URL:http://aclweb.org/anthology/P05-1045.
  2. Sandoval, A. M., and T. Redondo, "Text Analytics: the convergence of Big Data and Artificial Intelligence," International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 3, No. 6, 2016. DOI: http://dx.doi.org/10.9781/ijimai.2016.369.
  3. D. Boyd, and K. Crawford, "Six Provocations for Big Data," A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, 2011. DOI: http://dx.doi.org/10.2139/ssrn.1926431.
  4. J. Chang, "An Experimental Evaluation of Box office Revenue Prediction through Social Big data Analysis and Machine Learning," The Journal of The Institute of Internet, Broadcasting and Communication, Vol. 17, Issue 3, pp. 167-173, 2017. DOI: https://doi.org/10.7236/JIIBC.2017.17.3.167. https://doi.org/10.7236/JIIBC.2017.17.3.167
  5. J. KIM, Y. Jang, J. Min, “A Study on the Effect of School violence to Adolescent's School Adjustment : Moderating Effect of Parent-child Communication,” Korean Journal of Youth Studies, Vol. 18, No. 7, pp. 209-234, 2011. UCI:G704-000387.2011.18.7.005.
  6. B. Khu, “The Mediation Effects of relationship with parent, teacher, and peer between Self-efficacy and Adjustment to School,” Korean Journal of Youth Studies, Vol. 19, No. 3, pp. 347-373, 2012. UCI: G704-000387.2012.19.3.010.
  7. J. Kim, “The Longitudinal Relationship between School adjustment and Academic achievement in Adolescents on the Parenting attitude,” The Journal of Counseling. Korean Counseling Association (KCA), Vol. 17, No. 2, pp. 303-326, 2016. DOI: 10.15703/kjc.17.2.201604.303.
  8. Agrawal, R. and Srikant, R. "Fast Algorithms for Mining Association Rules in Large Databases," in Proc. of the 20th International Conference on Very Large Data Bases, pp. 487-499, 1994. ISBN:1-55860-153-8.
  9. National Youth Policy Institute, 1st 7th Survey Data User's guide in Korea Children and Youth Panel Survey(KCYPS), National Youth Policy Institute, Seoul, 2017. URL: http://archive.nypi.re.kr.
  10. Y. Kim, W. Kim, and U. Kim, "An Efficient Method for Mining Frequent Patterns based on Weighted Support over Data Streams," The Journal of Korea Academia-Industrial cooperation Society, Vol. 10, No. 8, pp. 1998-2004, 2009. UCI: G704-001653.2009.10.8.049. https://doi.org/10.5762/KAIS.2009.10.8.1998
  11. Brett Lantz, Machine Learning with R Kindle Edition, Packt, pp. 324-326, 2013. ASIN: B00G9581JM.
  12. Nada Hussein, Abdallah Alashqur, and Bilal Sowan, "Using the interestingness measure lift to generate association rules," Journal of Advanced Computer Science & Technology, Vol. 4, No. 1. pp. 156-161, 2015. DOI: http://dx.doi.org/10.14419/jacst.v4i1.4398. https://doi.org/10.14419/jacst.v4i1.4398
  13. Qaiser, Shahzad and Ali, Ramsha, "Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents," International Journal of Computer Applications, Vol. 181, No. 1, 2018. DOI: https://10.5120/ijca2018917395.
  14. S. Oh, “Design and Analysis of TSK Fuzzy Inference System using Clustering Method,” The Journal of Korea Institute of Information, Electronics, and Communication Technology, Vol. 7, No. 3, pp. 132-136, 2014. UCI: G704-SER000003092.2014.7.3.004. https://doi.org/10.17661/jkiiect.2014.7.3.132
  15. Aparna Upadhyay, Ravindra Gupta, and Varsha Namdev, "Clustering analysis based learning of Web Mining," International Journal of Advance Engineering and Research Development, Vol. 4, Issue 6, 2017. e-ISSN: 2348-4470, print-ISSN: 2348-6406.