IDEAS home Printed from https://ideas.repec.org/a/axf/eiaaaa/v2y2025i11p165-172.html

The Cultivation Pathway for Transferable Competencies in the Major of Big Data and Accounting: A Layered Contextualized Teaching Model Based on the CPA Competency Map

Author

Listed:
  • Jiao, Yilin

Abstract

This study addresses the challenge of cultivating transferable competencies within Big Data and Accounting education. Traditional teaching methods struggle to develop these competencies cohesively amidst rapid technological and regulatory changes. To bridge this gap, the research proposes a layered contextualized teaching model grounded in the CPA Competency Map. This model systematically translates the Competency Map's seven core transferable competencies into practical pedagogy by designing progressively complex teaching scenarios aligned with cognitive development stages. These scenarios progress from foundational standardized tasks for basic skills to strategic scenarios featuring open-ended, unstructured strategic problems. This tiered approach ensures competency development evolves from concrete operations to abstract strategic thinking. In addition, this study analyzes the situation gradient control and the cognitive intermediary function of the teacher's role, which provides guarantee for the implementation of teaching strategies. The model offers a structured, industry-relevant pathway for transferable competency development, moving beyond knowledge-focused instruction to enhance graduate adaptability.

Suggested Citation

  • Jiao, Yilin, 2025. "The Cultivation Pathway for Transferable Competencies in the Major of Big Data and Accounting: A Layered Contextualized Teaching Model Based on the CPA Competency Map," Education Insights, Scientific Open Access Publishing, vol. 2(11), pages 165-172.
  • Handle: RePEc:axf:eiaaaa:v:2:y:2025:i:11:p:165-172
    as

    Download full text from publisher

    File URL: https://soapubs.com/index.php/EI/article/view/889/867
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:axf:eiaaaa:v:2:y:2025:i:11:p:165-172. 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: Yuchi Liu (email available below). General contact details of provider: https://soapubs.com/index.php/EI .

    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.