The Journal of Bigdata (한국빅데이터학회지)

Korea Bigdata Society (한국빅데이터학회)
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Education Data and Analytics: A Review of the State of the Art

교육 데이터와 분석 기법: 사례 연구를 중심으로

  • 권영옥 (숙명여자대학교 경영학부)
  • Received : 2019.07.29
  • Accepted : 2019.08.30
  • Published : 2019.08.30
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Abstract

With the increase of education data, there have been many studies on the application of various analytics to improve students' performance and educational environments over the past decade. This paper first introduces the cases of universities that successfully utilize the analysis results and, more specifically, examines which data and analytical techniques are used for each analysis purpose. Based on the findings, the limitations of the current analytics and the direction of future analysis are discussed.

지난 십여 년간 학생의 학습 과정에서 생성되는 방대한 데이터를 다양한 분석기술을 적용하여 학생의 학습 성과 및 교육 환경을 개선하기 위한 관련 연구들이 활발히 진행되어 왔다. 본 논문에서는 교육데이터의 분석 결과를 성공적으로 활용하고 있는 대학의 사례들을 살펴본 후, 기존 사례 연구를 통해서 보다 구체적으로 분석 목적별로 어떤 데이터와 분석 기법이 사용되는지 정리하였다. 그리고 고찰 결과를 토대로 기존 분석의 한계점 및 향후 분석 방향에 대해 제안하고자 한다.

Keywords

References

  1. Frey, T. Communicating with the Future: How Re-engineering Intentions Will Alter the Master Code of Our Future, Da Vinci Institute Press, 2011.
  2. Papamitsiou, Z. and Economides, A.A. "Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence, "Journal of Educational Technology & Society, Vol.17, No. 4, pp.49-64. 2014.
  3. 권영옥. "빅데이터를 활용한 맞춤형 교육 서비스 활성화 방안연구. 한국지능정보시스템학회 학술대회논문집, pp. 90-96, 2013.
  4. Meehl, P..E. Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. MN, US: University of Minnesota Press. 1954.
  5. 정윤혁, 빅데이터와 교육분석 (Education Analytics). 미디어와 교육, 제5권, 제1호, pp.44-49, 2015.
  6. Ferguson, R. "Learning analytics: drivers, developments and challenges," International Journal of Technology Enhanced Learning, Vol.4, No.5/6, pp. 304-317, 2012. https://doi.org/10.1504/IJTEL.2012.051816
  7. Siemens, G. and Long, P. "Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, Vol.46, No.5, pp. 31-40, 2011.
  8. https://blog.simonassociates.net/what-makes-arizona-state-university-the-most-innovative-college-in-the-us
  9. http://www.sr.ithaka.org/publications/building-a-pathway-to-student-success-at-georgia-state-university.
  10. Shahiri, A. M. and Husain, W. "A review on predicting student's performance using data mining techniques," Procedia Computer Science, Vol.72, pp. 414-422, 2015. https://doi.org/10.1016/j.procs.2015.12.157
  11. Iqbal, Z., Qadir, J., Mian, A.N. and Kamiran, F. "Machine learning based student grade prediction: A case study," arXiv preprint arXiv:1708.08744, 2017.
  12. Manouselis, N., Drachsler, H., Verbert, K., and Santos, O.C. (Eds.). Recommender Systems for Technology Enhanced Learning: Research Trends and Applications. Springer Science & Business Media, 2014.
  13. Park, Y. "A Recommender System for Personalized Exploration of Majors, Minors, and Concentrations," In RecSys Posters, 2017.
  14. Elbadrawy A, Polyzou A, Ren Z, Sweeney M, Karypis G., and Rangwala H. "Predicting student performance using personalized analytics," Computer, Vol.49, No.4, pp:61-69, 2016. https://doi.org/10.1109/MC.2016.119
  15. Boyer, S. and Veeramachaneni, K. "Transfer Learning for Predictive Models in Massive Open Online Courses," In International conference on artificial intelligence in education, pp. 54-63, 2015.
  16. 최기성, 조민수 "대학 명성이 졸업생 취업 질에 미치는 효과와 시사점", 조사연구, 제17권, 제2호, 119-162, 2016.
  17. 염동기, 문상규, 박성수. "대학졸업자의 취업성과 결정요인에 관한 실증분석," 취업진로연구. 제7권, 제4호, pp.45-68, 2017.
  18. 김종율, 노광현. "AI 를 활용한 대학생 진로조언 시스템 모델 및 데이터 수집과 융합에 대한 연구," 디지털융복합연구, 제17권, 제2호, pp. 177-185, 2019. https://doi.org/10.14400/jdc.2019.17.2.177
  19. Rienties, B., Cross, S., and Zdrahal, Z. Implementing a learning analytics intervention and evaluation framework: What works?. In Big data and learning analytics in Higher Education (pp. 147-166). Springer, Cham, 2017.