Smart Teaching Systems: A Hybrid Framework of Reinforced Learning and Deep Learning

Authors

  • Yijiao Sun
  • Wei Huang
  • Zhiwen Wang
  • Xiaofeng Xu
  • Min Wen
  • Pei Wu

DOI:

https://doi.org/10.3991/ijet.v18i20.44217

Keywords:

smart teaching system, vocational education, reinforced learning, deep learning, hybrid framework, retrieval attention mechanism, prediction of personal needs

Abstract


As vocational education is transforming constantly, there is an urgent demand in the field of education for smart teaching systems to be able to respond to students’ personal learning needs in a more dynamic way, but a review of currently available algorithms reveals that the common application of existing methods lacks a deep enough understanding of students’ individual differences. Out of these concerns, this study aims to propose a novel and hybrid framework for the design of smart teaching systems based on reinforced learning and deep learning, so as to overcome the shortcomings of existing research and more accurately predict students’ personal needs. Besides, an end-to-end model with a retrieval attention mechanism has been designed for generating responses with precise information about students’ learning needs. This study provides a smart teaching scheme for vocational education that is new, efficient, and humane, while also providing a solid theoretical foundation for the reform and innovation of the education system in the future.

Cover iJET – Vol 18, No 20, 2023

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Published

2023-10-17

How to Cite

Sun, Y. ., Huang, W. ., Wang, Z. ., Xu, X., Wen, M. ., & Wu, P. . (2023). Smart Teaching Systems: A Hybrid Framework of Reinforced Learning and Deep Learning. International Journal of Emerging Technologies in Learning (iJET), 18(20), pp. 37–50. https://doi.org/10.3991/ijet.v18i20.44217

Issue

Vol. 18 No. 20 (2023)

Section

Papers

License

Copyright (c) 2023 Nan Zhang (Submitter); Yijiao Sun, Wei Huang, Zhiwen Wang, Xiaofeng Xu, Min Wen, Pei Wu

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