IDEAS home Printed from https://ideas.repec.org/a/igg/jwltt0/v18y2023i2p1-15.html
   My bibliography  Save this article

An Evaluation Model of Preschool Teacher Talent Training Based on Big Data Technology

Author

Listed:
  • Fang Wang

    (Ma An Shan Teachers College, China)

  • Shasha Xu

    (Zhengzhou Preschool Education College, China)

Abstract

The key to improving the quality of preschool education lies in high-quality preschool teachers, and high-quality preschool teachers in turn depend on high-quality teacher education. A pilot project to “strengthen the training of kindergarten instructors” was recommended by the Ministry of Education. This research studies the resident training mode implemented by pilot normal colleges and universities, uses big data technology to collect the required data, and uses neural networks to evaluate the talent training mode of preschool teachers, and finally completes the following work: 1) This article introduces the research progress of the talent training mode of preschool teachers at home and abroad; 2) Introduced are the RBF neural network's fundamental ideas and algorithmic stages, and the evaluation index system for the training of preschool teachers is established; and 3) Experiment to determine the best model parameters, then feed the test data into the model you trained, and compare the output against expert evaluations.

Suggested Citation

  • Fang Wang & Shasha Xu, 2023. "An Evaluation Model of Preschool Teacher Talent Training Based on Big Data Technology," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 18(2), pages 1-15, February.
    • Handle: RePEc:igg:jwltt0:v:18:y:2023:i:2:p:1-15
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWLTT.334361
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei Zheng & Yu-Yu Ma & Hung-Lung Lin, 2021. "Research on Blended Learning in Physical Education During the COVID-19 Pandemic: A Case Study of Chinese Students," SAGE Open, , vol. 11(4), pages 21582440211, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chia-Liang Lin & Jwu-Jenq Chen & Yu-Yu Ma, 2023. "Ranking of Service Quality Solution for Blended Design Teaching Using Fuzzy ANP and TOPSIS in the Post-COVID-19 Era," Mathematics, MDPI, vol. 11(5), pages 1-28, March.
    2. Louis Moustakas & Denise Robrade, 2022. "The Challenges and Realities of E-Learning during COVID-19: The Case of University Sport and Physical Education," Challenges, MDPI, vol. 13(1), pages 1-12, March.

    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:igg:jwltt0:v:18:y:2023:i:2:p:1-15. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.