IDEAS home Printed from https://ideas.repec.org/a/igg/jwltt0/v21y2026i1p1-20.html

Evaluation and Optimization of STEAM Course Teaching Effect Based on Deep Learning

Author

Listed:
  • Yangyang Gui

    (Shaanxi Fashion Engineering University, China)

  • Xiao Wang

    (Shaanxi Fashion Engineering University, China)

  • Bing Wang

    (Shaanxi Fashion Engineering University, China)

Abstract

With more than 70% of educational institutions around the world implementing STEAM courses, the evaluation of their teaching effectiveness is full of problems, and about 65% of schools lack unified and effective evaluation standards. This study constructs the STEAM-DeepEval model to accurately evaluate the teaching effectiveness of STEAM courses. The experiment selects multi-school multi-dimensional teaching data sets, covering learning performance data in science, technology, and other fields. The results show that the STEAM-DeepEval model has a low MSE indicator of 0.056 in dataset subset 1, which is much lower than the 0.123 of the decision tree model; the MAE indicator is also excellent; the mean square error value is close to 0.93 in scientific subject evaluation and other aspects. The model demonstrates strong advantages by virtue of knowledge graph embedding, dynamic feature fusion, and teaching effect prediction module. This study provides a scientific method for the evaluation of STEAM course teaching effects and helps improve teaching quality.

Suggested Citation

  • Yangyang Gui & Xiao Wang & Bing Wang, 2026. "Evaluation and Optimization of STEAM Course Teaching Effect Based on Deep Learning," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global Scientific Publishing, vol. 21(1), pages 1-20, January.
    • Handle: RePEc:igg:jwltt0:v:21:y:2026:i:1:p:1-20
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWLTT.410068
    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:jwltt0:v:21:y:2026:i:1:p:1-20. 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.