IDEAS home Printed from https://ideas.repec.org/a/ids/ijnvor/v28y2023i2-3-4p318-336.html
   My bibliography  Save this article

A study on the development of English reading skills in the MOOC model of English language teaching

Author

Listed:
  • Li Ling

Abstract

This study proposes a personalised intelligent reading resource recommendation method based on MOOC mode. This method uses a deep belief network (DBN) model to extract students' reading interests and other related data features, and uses the K-means algorithm to classify users' interests. The model is applied to a personalised recommendation system in the MOOC environment. When the training set accounts for 100%, 75%, 50%, and 25% of the total dataset, the root mean square errors of the recommendation results of the DBN algorithm are 78%, 83%, 88%, and 96%, respectively. During the training process, the convergence speed of the DBN algorithm is significantly faster, with a minimum root mean square error value of 0.805. In the evaluation of recommendation effectiveness under different indicators, DBN performs the best, indicating that the model can adapt to various situations and has great practical application value.

Suggested Citation

  • Li Ling, 2023. "A study on the development of English reading skills in the MOOC model of English language teaching," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 28(2/3/4), pages 318-336.
  • Handle: RePEc:ids:ijnvor:v:28:y:2023:i:2/3/4:p:318-336
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=133861
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijnvor:v:28:y:2023:i:2/3/4:p:318-336. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=22 .

    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.