IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/6696155.html
   My bibliography  Save this article

Evaluation of the Established IoT Smart Home Robot Teaching Module Based on Embedded Thematic-Approach Strategy

Author

Listed:
  • Kai-Chao Yao
  • Wei-Tzer Huang
  • Li-Chiou Hsu
  • Chin-Kun Yang
  • Jian-Yuan Lai

Abstract

Artificial intelligence (AI) technology–based intelligent robots are constructed using different technologies, such as Internet of Things (IoT), big data, deep learning, machine learning, neural network, and expert system. This particular type of robots can increase the work efficiency of humans and improve their quality of life. From the industry perspective, AI robots possess unlimited potential for development, and they are projected to be a 10-trillion-dollar industry. In this study, the critical technology of IoT is applied to develop a teaching module for an IoT smart home robot. Teaching and evaluation are performed through an embedded thematic-approach teaching strategy in the course named Automatic Measurement and Monitoring. This research aims to teach students how to integrate IoT technology into robot design and construction to build IoT smart home robots. This cross-disciplinary research incorporates emerging technology—integration of smart home, robot construction, and IoT technologies—into industrial education, teaching material and equipment development, and experimental teaching and evaluation. The participating students were juniors or seniors from the Department of Electrical Engineering or Electromechanical Engineering at the University of Technology.

Suggested Citation

  • Kai-Chao Yao & Wei-Tzer Huang & Li-Chiou Hsu & Chin-Kun Yang & Jian-Yuan Lai, 2020. "Evaluation of the Established IoT Smart Home Robot Teaching Module Based on Embedded Thematic-Approach Strategy," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, December.
  • Handle: RePEc:hin:jnlmpe:6696155
    DOI: 10.1155/2020/6696155
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2020/6696155.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2020/6696155.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2020/6696155?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:6696155. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.