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Intelligent retrieval method of mobile learning resources in the intelligent higher education system

Author

Listed:
  • Liqing Zhang

    (Changchun University)

  • Xiaowen Yu

    (Changchun University)

Abstract

Mobile learning has become more important for the new generation. It helps students think better, pushes them to study more deeply, and leads them to significant knowledge production. Mobile learning (mobile learning) is a learning paradigm that enables students to get resources from mobile technology and the Internet everywhere and anytime. The mobile learning components should be appropriately arranged. The interactions between the different components should be combined effectively and optimally for m-learning to be successful and effective. It is important to arrange the features of mobile learning and how they are applied to mobile learning activities, the application procedures, and the duration of the application time well in advance. In this paper, Human-interaction machine-based intelligent retrieved (HIM-IR) method has been suggested to improve student performance using mobile education. In mobile learning, students would find information through the network. Thus, the retrieval of quality information in support services is quite crucial. Mobile intelligent recovery would help the intelligence engine for mobile learning. The existing web server has technology on the server that doesn't have great precision and intelligence. The input string format is necessary for the retrieval process. The proposed methods aim to define the fundamental aspects and features of mobile learning in new trends in technological development. HIM-IR can be beneficial for anyone engaged in mobile learning design, preparation, and implementation.

Suggested Citation

  • Liqing Zhang & Xiaowen Yu, 2022. "Intelligent retrieval method of mobile learning resources in the intelligent higher education system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3079-3091, December.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:6:d:10.1007_s13198-021-01455-7
    DOI: 10.1007/s13198-021-01455-7
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    References listed on IDEAS

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    1. P Mohamed Shakeel & S Baskar, 2020. "Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS): Human Emotion Classification," International Journal of Technology and Human Interaction (IJTHI), IGI Global, vol. 16(1), pages 94-104, January.
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