DOI QR코드

DOI QR Code

A Study on the Data Collection and Convergence of Career Advisor System Using AI

AI를 활용한 대학생 진로 조언 시스템 모델 및 데이터 수집과 융합에 대한 연구

  • Kim, Jong-yul (Division of Smart Convergence Consulting, Hansung University) ;
  • Ro, Kwang-hyun (Division of IT Convergence Engineering, Hansung University)
  • 김종율 (한성대학교 스마트융합컨설팅) ;
  • 노광현 (한성대학교 IT융합공학부)
  • Received : 2018.12.24
  • Accepted : 2019.02.20
  • Published : 2019.02.28

Abstract

The purpose of this study is to investigate the causes of career problems, which are the biggest problems of Korean university students, and to solve them by using case studies of domestic and global universities, I would like to suggest a career advisor system model for college students. It is most important to collect advice and learning data to solve the career problems of college students by utilizing information technology such as data analysis and AI. Research has not been actively pursued because the university has very limited internal data to advise on career problems. In this paper, we study the data types and methods of college students' career advice, and propose a career advisor counseling system for college students.

본 연구는 국내 대학생들의 가장 큰 고민인 진로 문제에 대한 원인과 이를 해결하기 위한 국내 외 대학의 정보기술을 활용한 문제해결 사례 연구와 진로 조언을 위한 데이터 수집 유형 및 수집방법 연구를 통해 AI를 활용한 대학생 진로조언 시스템 모델을 제안하고자 한다. 데이터 분석 및 AI와 같은 정보기술을 활용하여 대학생들의 진로 문제 해결을 위해서는 조언과 학습을 위한 데이터의 수집이 가장 중요하다. 그러나 대학들 역시 학생들에게 진로 문제에 대해 조언할 수 있는 내부 데이터의 부족으로 정보기술을 활용한 진로 문제 해결방법에 대한 연구가 활발히 진행되지 못하고 있다. 본 논문에서는 대학생들의 진로 조언을 위해 공공 데이터와 대학 내부 민간기관 지자체에서 수집 가능한 진로 조언 데이터의 유형과 수집 방법 및 활용 방안에 대한 연구와 이를 활용하여 대학생 진로조언 시스템 모델을 제안하고자 한다.

Keywords

DJTJBT_2019_v17n2_177_f0001.png 이미지

Fig. 1. ASU’s online advising system

DJTJBT_2019_v17n2_177_f0002.png 이미지

Fig. 2. SKKU’s kingobot

DJTJBT_2019_v17n2_177_f0003.png 이미지

Fig. 3. AI use career advice and data processing model

Table 1. Worknet OpenAPI provided list

DJTJBT_2019_v17n2_177_t0001.png 이미지

Table 2. Search job information (worknet.go.kr)

DJTJBT_2019_v17n2_177_t0002.png 이미지

Table 3. Data collection for developing student job competency

DJTJBT_2019_v17n2_177_t0003.png 이미지

Table 4. Collect university internal data

DJTJBT_2019_v17n2_177_t0004.png 이미지

Table 5. Advice system Q&A data example

DJTJBT_2019_v17n2_177_t0005.png 이미지

Table 6. Career Advisor System Layer

DJTJBT_2019_v17n2_177_t0006.png 이미지

Table 7. career Advisor System simulation process

DJTJBT_2019_v17n2_177_t0007.png 이미지

References

  1. H. J. Jang. (2017). Survey on College Career Education(2017). Sejong : Ministry of Education.
  2. J. Y. Lee. (2017). Direction of Career Education Policy and Career Education for the Fourth Industrial Revolution. Sejong : KRIVET.
  3. S. K. Yoon. (2015). A Study on the Factors that Affect College and Major Selection. The Journal of Korean Education, 42(2), 87-107. https://doi.org/10.22804/JKE.2015.42.2.004
  4. J. Y. Lee. (2017). Overseas Career Development Policy and Direction of Career Education in Korea. Sejong : KRIVET.
  5. Ministry of Education. (2016). Second career education 5-year basic plan. Sejong : Ministry of Education.
  6. K. Y. Ji & J. Y. Han. (2016). A Study on the Comparative study for the Four-year Collegiate Career Preparation Behavior by Grade Level : The case of C University. Journal of Digital Convergence, 14(6), 33-41. DOI : 10.14400/JDC.2016.14.6.33
  7. D. K. Lim. (2016). The Influence of Major Selection Motive on Major Satisfaction, Instruction Participation, Employability, Employment Strategies. Journal of Employment and Career Association, 6(14), 85-109. https://doi.org/10.35273/jec.2016.6.4.005
  8. Ministry of Education. (2018). Reorganization plan of university financial support for university autonomy and competitiveness. Sejong : Ministry of Education.
  9. Y. H. Ko & Y. H. Park. (2018). The Effects of Career Decision Making Self-Efficacy and Career Maturity on the Senior Students′ Employment Stress. Journal of Digital Convergence, 16(1), 73-83. DOI : 10.14400/JDC.2018.16.1.073
  10. E. D. Phillips. (2013). Improving advising using technology and data analytics. Change: The Magazine of Higher Learning, 45(1), 48-55. https://doi.org/10.1080/00091383.2013.749151
  11. N. Y. Kim. (2018). A Study on Chatbots for Developing Korean College Students. Journal of Digital Convergence, 16(8), 19-26. DOI : 10.14400/JDC.2018.16.8.019
  12. L. C. Page & H. Gehlbach. (2017). How an artificially intelligent virtual assistant helps students navigate the road to college. AERA Open, 3(4), 2332858417749220. https://doi.org/10.1177/2332858417749220
  13. B. Williamson. (2018). The hidden architecture of higher education: building a big data infrastructure for the 'smarter university'. International Journal of Educational Technology in Higher Education, 15(1), 12. https://doi.org/10.1186/s41239-018-0094-1
  14. NIA. (2018). Analysis and development prospect of domestic and foreign trends of artificial intelligence-based chatbot service. Daegu : NIA.
  15. Y. O. Kwon. (2013). Data Analytics in Education : Current and Future Directions. Journal of intelligence and information systems, 19(2), 87-99. https://doi.org/10.13088/jiis.2013.19.2.087
  16. S. H. Hong. (2018). Private information protection method and countermeasures in Big-data environment: Survey. Journal of the Korea Convergence Society, 9(10), 55-59. https://doi.org/10.15207/JKCS.2018.9.10.055
  17. H. J. Kang & S. I. Kim. (2017). Evaluation on the Usability of Chatbot Intelligent Messenger Mobile Services -Focusing on Google(Allo) and Facebook(M messenger). Journal of the Korea Convergence Society, 8(9), 271-276. https://doi.org/10.15207/JKCS.2018.9.10.055