IDEAS home Printed from https://ideas.repec.org/a/igg/jthi00/v18y2022i7p1-15.html
   My bibliography  Save this article

Self-Regulated Learning and Scientific Research Using Artificial Intelligence for Higher Education Systems

Author

Listed:
  • Xixi Huang

    (Zhejiang College of Security Technology, China)

  • Lihui Dong

    (Wenzhou Institute of Technology, China)

  • Chandru Vignesh C.

    (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India)

  • Praveen Kumar D.

    (Hindustan University, India)

Abstract

Self-regulated learning (SRL) is a very significant ability for students in the learning process, and SRL strategies are used to assist students in learning efficiently in higher education. Many SRL environments face challenges and barriers, including less immediate feedback and guidance, a high level of self-personalization, environment, and resources from teachers or learner designers. Hence, in this paper, the artificial intelligence framework for self-regulated learning (AIF-SRL) overcame the SRL environment's challenges in higher education. Self-regulated students are active participants in their learning and may select from a strategic portfolio and monitor their progress towards the objective. The proposed artificial intelligence-based solution has been used to monitor student behavior through students' feedback and responses. The experimental results show that the proposed AIF-SRL method improves the student's self-evaluation, self-regulation behavior, self-efficacy, learning gain, and self-satisfaction compared to other existing methods.

Suggested Citation

  • Xixi Huang & Lihui Dong & Chandru Vignesh C. & Praveen Kumar D., 2022. "Self-Regulated Learning and Scientific Research Using Artificial Intelligence for Higher Education Systems," International Journal of Technology and Human Interaction (IJTHI), IGI Global, vol. 18(7), pages 1-15, June.
    • Handle: RePEc:igg:jthi00:v:18:y:2022:i:7:p:1-15
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJTHI.306226
    Download Restriction: no
    ---><---

    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:jthi00:v:18:y:2022:i:7: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.

    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: 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.