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http://dx.doi.org/10.9728/dcs.2018.19.6.1115

Study for Prediction System of Learning Achievements of Cyber University Students using Deep Learning based on Autoencoder  

Lee, Hyun-Jin (Division of ICT Engineering, Korea Soongsil Cyber University)
Publication Information
Journal of Digital Contents Society / v.19, no.6, 2018 , pp. 1115-1121 More about this Journal
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.
Keywords
Autoencoder; Deep Learning; Learning Management System; E-learning; Learning Analytics;
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1 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 .   DOI
2 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.
3 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.   DOI
4 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 .
5 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.   DOI
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 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.   DOI
10 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.
11 Korea Education And Research Information Service, White paper on ICT in education korea, 2017.
12 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.
13 Ministry of Education. Standard of System for Cyber Education [Internet]. Available: http://www.law.go.kr/LSW//admRulInfoP.do?admRulSeq=2100000055170.
14 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
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.   DOI
16 C. Szegedy, T. Alexander and E. Dumitru, "Deep neural networks for object detection," Advances in Neural Information Processing Systems, 2013.
17 J. F. Wiley, "R Deep Learning Essentials," Packt Publishing, 2016.
18 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.
19 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.