IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/7369920.html
   My bibliography  Save this article

Construction and Practice of Multiple Mixed Teaching Mode Based on Big Data Analysis: A Case Study of “International Trade†Course

Author

Listed:
  • Xiaoyuan Wu
  • Zaoli Yang

Abstract

With the progress of society, the quality requirements of international business enterprises for international business talents have been improved accordingly. So, it is urgent to conduct in-depth research on the teaching model and the improvement of students’ practical ability. Taking international business as an example, this paper analyzes the contradiction between the supply and demand of international business technical talents by literature research. Furthermore, the convolution neural network model is used to improve the consistency between the talent cultivation of international business major and the talent demand of enterprises by interviewing teachers and questionnaire survey of students. By studying how to implement the professional training curriculum system construction and enterprise to talented person ability training requirements cohesion, this paper in view of the secondary vocational school of international business in today’s society professional training curriculum system was modified and perfected. The ultimate purpose of this paper is to meet the demand of international business for characteristic talents and constantly promote the high-quality development of international business education.

Suggested Citation

  • Xiaoyuan Wu & Zaoli Yang, 2022. "Construction and Practice of Multiple Mixed Teaching Mode Based on Big Data Analysis: A Case Study of “International Trade†Course," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-10, June.
  • Handle: RePEc:hin:jnddns:7369920
    DOI: 10.1155/2022/7369920
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/ddns/2022/7369920.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/ddns/2022/7369920.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/7369920?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:jnddns:7369920. 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.