Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
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Study for Prediction System of Learning Achievements of Cyber University Students using Deep Learning based on Autoencoder

오토인코더에 기반한 딥러닝을 이용한 사이버대학교 학생의 학업 성취도 예측 분석 시스템 연구

  • Lee, Hyun-Jin (Division of ICT Engineering, Korea Soongsil Cyber University)
  • 이현진 (숭실사이버대학교 ICT공학부)
  • Received : 2018.05.25
  • Accepted : 2018.06.25
  • Published : 2018.06.30
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Abstract

In this paper, we have studied a data analysis method by deep learning to predict learning achievements based on accumulated data in cyber university learning management system. By predicting learner's academic achievement, it can be used as a tool to enhance learner's learning and improve the quality of education. In order to improve the accuracy of prediction of learning achievements, the autoencoder based attendance prediction method is developed to improve the prediction performance and deep learning algorithm with ongoing evaluation metrics and predicted attendance are used to predict the final score. In order to verify the prediction results of the proposed method, the final grade was predicted by using the evaluation factor attendance data of the learning process. The experimental result showed that we can predict the learning achievements in the middle of semester.

본 논문에서는 사이버대학교 학습관리시스템에 누적된 데이터를 기반으로 학습 성과를 예측하기 위하여 딥러닝에 기반한 데이터 분석 방법을 연구하였다. 학습자의 학업 성취도를 예측하면, 학습자의 학습을 촉진하여 교육의 질을 높일 수 있는 도구로 활용될 수 있다. 학습 성과의 예측의 정확도를 향상시키기 위하여 오토인코더에 기반하여 한학기 출결 상황을 예측하고, 학기 진행 중인 평가 요소들과 결합하여 딥러닝으로 학습하여 최종 예측의 정확도를 높였다. 제안하는 예측 방법을 검증하기 위하여 학습 진행 과정의 출결데이터의 예측과 평가요소 데이터를 활용하여 최종학습 성취도를 예측하였다. 실험을 통하여 학기 진행중에 학습자의 성취도를 예측할 수 있는 것을 보였다.

Keywords

References

  1. Ministry of Education. Standard of System for Cyber Education [Internet]. Available: http://www.law.go.kr/LSW//admRulInfoP.do?admRulSeq=2100000055170.
  2. G. Y. Ryu, "Quality Improvement Plan of Elementary School Teacher's Distance Training," Journal of the Korean Association of Information Education, Vol. 9 No. 4, pp.617-625, 2005
  3. S. K. Lee and Y. J. Park, "Case Study on Application of Social Learning in Workforce Education," Journal of Digital Contents Society, Vol. 16, No. 4, pp. 523-534, 2015 . https://doi.org/10.9728/dcs.2015.16.4.523
  4. J. M. Kim, Y. Kim, W. G. Lee, "A Study for Improvement of Learning Management System in Distance Education & Training Institutes", Journal of academia-industrial technology, Vol. 11 No. 4, pp. 1411-1418, 2010.
  5. J. Y. Jung and J. W. Lee, "An Exploratory Study on Dropout Intention of Cyber University Students," Korean Education Inquiry , Vol. 35, No. 4, pp. 149-168, 2017. https://doi.org/10.22327/kei.2017.35.4.149
  6. E. J. Song, "A Study on the System for On-line Education by Mobile," Journal of Digital Contents Society Vol. 6, No. 3, pp. 149-155, 2005 .
  7. K. S. Noh, "Convergence Analysis of Recognition and Influence on Bigdata in the e-Learning Field," Journal of Digital Convergence, Vol. 13, Issue 10, pp. 51-58, 2015. https://doi.org/10.14400/JDC.2015.13.10.51
  8. M. L. Ahn, Y. Y. Choi, Y. H. Bae, Y. M. Ko, M. H. Kim, "A Literature Review on Learning Analytics: Exploratory study of empirical researches utilizing log data in Korea," Journal of Educational Technology, Vol. 32, No. 2, pp. 253-291, 2016. https://doi.org/10.17232/KSET.32.2.253
  9. M. D. Pistilli, ans K. E. Arnold, " In practice: Purdue Signals: Mining real-time academic data to enhance student success ," About Campus, Vol. 15, No. 3, pp. 22-24, 2010. https://doi.org/10.1002/abc.20025
  10. I. H. Jo, J. H. Kim, "Investigation of Statistically Significant Period for Achievement Prediction Model in e-Learning," Journal of Educational Technology, Vol. 29 No. 2, pp. 285-306, 2013. https://doi.org/10.17232/KSET.29.2.285
  11. Y. J. Park, "Need Analysis for Learning Analytics Dashboard in LMS: Applying Activity Theory as an Analytic and design Tool," Journal of Educational Technology, Vol. 30, No. 2, pp. 221-258, 2014. https://doi.org/10.17232/KSET.30.2.221
  12. J, W, You, "Computer Education Curriculum and Instruction : Dropout Prediction Modeling and Investigating the Feasibility of Early Detection in e-Learning Courses," The Journal of Korean association of computer education, Vol. 17, No. 1, pp. 1-12, 2014.
  13. Korea Education And Research Information Service, White paper on ICT in education korea, 2017.
  14. T. Tran, H. Dang, V. Dinh, T. Truong, T. Vuong and X. Phan, "Performance Prediction for Students: A Multi-Strategy Approach," The Journal of Institute of Information and Communication Technologies of Bulgarian Academy of Sciences, Vol. 17, No. 2, pp. 164-182, 2017.
  15. W. G. Hatcher and W. Yu, "A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends," IEEE Access, Vol. 6, pp. 24411-24432, 2018. https://doi.org/10.1109/ACCESS.2018.2830661
  16. Y. Bengio, A. Couville, ad P. Vincent, "Representation Learning: A Review and New Perspectives," IEEE Trans. PAMI, special issue Learning Deep Architectures, Vol. 35, Issue 8, pp. 1798-1828, 2013.
  17. Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, Vol. 86, No. 11, pp. 278-2324, 1998.
  18. C. Szegedy, T. Alexander and E. Dumitru, "Deep neural networks for object detection," Advances in Neural Information Processing Systems, 2013.
  19. J. F. Wiley, "R Deep Learning Essentials," Packt Publishing, 2016.

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