IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i8p1277-d792096.html
   My bibliography  Save this article

CAC: A Learning Context Recognition Model Based on AI for Handwritten Mathematical Symbols in e-Learning Systems

Author

Listed:
  • Sung-Bum Baek

    (Department of e-Learning, Graduate School, Korea National Open University, Seoul 03087, Korea)

  • Jin-Gon Shon

    (Department of Computer Science, Korea National Open University, Seoul 03087, Korea)

  • Ji-Su Park

    (Department of Computer Science and Engineering, Jeonju University, Jeonju 55069, Korea)

Abstract

The e-learning environment should support the handwriting of mathematical expressions and accurately recognize inputted handwritten mathematical expressions. To this end, expression-related information should be fully utilized in e-learning environments. However, pre-existing handwritten mathematical expression recognition models mainly utilize the shape of handwritten mathematical symbols, thus limiting the models from improving the recognition accuracy of a vaguely represented symbol. Therefore, in this paper, a context-aided correction (CAC) model is proposed that adjusts an output of handwritten mathematical symbol (HMS) recognition by additionally utilizing information related to the HMS in an e-learning system. The CAC model collects learning contextual data associated with the HMS and converts them into learning contextual information. Next, contextual information is recognized through artificial intelligence to adjust the recognition output of the HMS. Finally, the CAC model is trained and tested using a dataset similar to that of a real learning situation. The experiment results show that the recognition accuracy of handwritten mathematical symbols is improved when using the CAC model.

Suggested Citation

  • Sung-Bum Baek & Jin-Gon Shon & Ji-Su Park, 2022. "CAC: A Learning Context Recognition Model Based on AI for Handwritten Mathematical Symbols in e-Learning Systems," Mathematics, MDPI, vol. 10(8), pages 1-22, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1277-:d:792096
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/8/1277/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/8/1277/
    Download Restriction: no
    ---><---

    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:gam:jmathe:v:10:y:2022:i:8:p:1277-:d:792096. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.