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Best Practices on Educational Service Platform with AI Approach

  • Hong, Je Seong (SE Lab, Dept. of Software and Communications Engineering, Hongik University) ;
  • Park, Bo Kyung (SE Lab, Dept. of Software and Communications Engineering, Hongik University) ;
  • Kwak, Jeil (Jeil Edus) ;
  • Kim, R. Young Chul (SE Lab, Dept. of Software and Communications Engineering, Hongik University) ;
  • Son, Hyun Seung (Reliability Technology Institute)
  • Received : 2019.09.16
  • Accepted : 2019.09.29
  • Published : 2019.12.31

Abstract

The current education is becoming more extensive with the application of various teaching methods. This is a problem that is so distributed that it is difficult for users to find the data and it takes a long time to find the information they need. Currently, various educational services, materials, and instruments are developed and scattered. Therefore, it is important to raise students' awareness of aptitude and career path with customized education tailored to students. Conventional education platforms have very difficult to choose the right materials for students because of the spread of educational programs and institution materials. To solve this, we propose a customized recommendation approach to recommend customized educational service materials and institution for students to teachers, which helps teachers conveniently choose materials suitable for their respective environments. On this new platform, the CNN algorithm provides recommended content for classes and students. For real service on the educational service platform, we implement this system for Jeil edus business. Through this mechanism, we expect to improve the quality of education by helping to select the right service.

Keywords

References

  1. M. Oquab, L. Bottou, I. Laptev and J. Sivic. "Learning and transferring mid-level image representations using convolutional neural networks", 2014 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp.1717-1724, 2014 DOI: https://doi.org/10.1109/CVPR.2014.222
  2. Yeongsu Kim, Seungwoo Lee (2018). "Combinations of Text Preprocessing and Word Embedding Suitable for Neural Network Models for Document Classification", Journal of KIISE, Vol. 45, No. 7, pp. 690-700, 2018. 7 DOI: https://doi.org/10.5626/JOK.2018.45.7.690
  3. Yoon Kim, "Convolutional Neural Networks for Sentence Classification", Journal of EMNLP, 25 Aug 2014 DOI: https: //doi.org/10.3115/v1/D14-1181
  4. P. Arena, L. Fortuna, L. Occhipinti, "A CNN algorithm for real time analysis of DNA microarrays", IEEE Transactions on Circuits and Systems I: Fundamental Theory and Application, Vol. 49, No. 3, pp.335-340, Mar 2002 DOI: https: //doi.org/10.1109/81.989167
  5. P.L. Venetianer, F. Werblin, T. Roska, L.O. ChuaAnalogic,"CNN algorithms for some image compression and restoration tasks", IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications. Vol. 42, No. 5, pp.278-284, May 1995 DOI: https: //doi.org/10.1109/81.386161