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진로교육을 위한 희망진로 예측프로그램 설계

Design of a Hopeful Career Forecasting Program for the Career Education

  • Kim, Geun-Ho (Department of Computer Education, Kongju National University) ;
  • Kim, Eui-Jeong (Department of Computer Education, Kongju National University)
  • 투고 : 2018.04.15
  • 심사 : 2018.06.16
  • 발행 : 2018.08.31

초록

4차 산업혁명을 맞이하여 학교 교육에 있어서 진로교육의 문제가 크게 대두되고 있다. 일선 현장에서도 인공지능 및 빅 데이터들을 효과적으로 처리하기 위한 서비스 또는 기술에 대하여 다양한 연구가 진행되고 있으나, 교육분야에 있어서는 학생들에 대한 데이터들을 단순처리과정을 거칠 뿐이다. 이에 본 논문에서는 인공지능 및 빅데이터를 활용한 학생들의 진로교육을 위한 진로 예측 프로그램을 설계 제시하고자 한다. 영재교육원 학생들의 관찰데이터를 이용하여 의사결정 트리중 가장 인공지능에 가깝고 효과적이라고 알려진 C4.5알고리즘으로 의사결정 트리를 구성하고 학생들의 희망 진로를 예측하는 것이다. 판별결과 카파계수는 0.7을 넘어 상당한 일치도를 보였고 평균절대오차도 0.1정도로 상당히 낮은 수치를 보였다. 이에 따라서 본 연구에서 보이듯이 많은 연구 및 데이터를 구축하여 학생들의 상담에 활용 진로를 제시하고 수업태도 및 방향을 제시하는데 도움이 될 것으로 사료된다.

In the wake of the 4th Industrial Revolution, the problem of career education in schools has become a big issue. While various studies are being conducted on services or technologies to effectively handle artificial intelligence and big data, in the field of education, data on students is simply processed. Therefore, in this paper, we are going to design and present career prediction programs for students using artificial intelligence and big data. Using observational data from students at the institute, the decision tree is constructed with the C4.5 algorithm known to be most intelligent and effective in the decision tree and is used to predict students' path of hope. As a result, the coefficient of kappa exceeded 0.7 and showed a fairly low average error of 0.1 degrees. As shown in this study, a number of studies and data will be deployed to help guide students in their consultation and to provide them with classroom attitudes and directions.

키워드

참고문헌

  1. M. S. Lee, "The Effect of Reasons of College Major Selection and Stresses of College Life on Career Confidence according to Experience of Gifted Education," Secondary Education Research, vol. 66, no. 1, pp. 229-255, Mar. 2011.
  2. D. J. Kim, D. Sharma, "Implementation of Decision Based Fruits Protection System Using Classification and Clustering Techniques," Asia-pacific Journal of Convergent Research Interchange, vol. 2, no. 4, pp. 23-31, Dec. 2016.
  3. S. H. Song, E. J. Kim, "The Recognition of Cyber Education and Development Plan of Chungcheongnam-do Civil Servants," Journal of the Korea Institute Of Information and Communication Engineering, vol. 21, no. 11, pp. 2184-2190, Nov. 2017. https://doi.org/10.6109/JKIICE.2017.21.11.2184
  4. L. Brett, Machine learning with R, 2th ed. Seoul, Seoul: Acornpub, 2017.
  5. J. Ramos, D. C. Avila, and J. Morales "Induction of Decision Trees Using an Internal Control of Induction," Lecture Notes in Computer Science, vol. 3512, pp. 795-803, Jun. 2005.
  6. H. W. Yim, "Security education and research in accordance with the paradigm shift in the industry Security," Journal of Security Engineering, vol. 12, no. 6 pp. 597-608, Dec. 2015. https://doi.org/10.14257/jse.2015.12.03
  7. J. H. Seo, "A Comparative Study on the Classification of the Imbalanced Intrusion Detection Dataset Based on Deep Learning," Journal of Korean Institute of Intelligent Systems, vol. 28, no. 2, pp. 152-159, Apr. 2018. https://doi.org/10.5391/JKIIS.2018.28.2.152