IDEAS home Printed from https://ideas.repec.org/h/spr/conchp/978-3-030-85254-2_17.html
   My bibliography  Save this book chapter

A Path Analysis of Learning Approaches, Personality Types and Self-Efficacy

In: Advances in Econometrics, Operational Research, Data Science and Actuarial Studies

Author

Listed:
  • Mine Aydemir

    (Bursa Uludağ University)

  • Nuran Bayram Arlı

    (Bursa Uludağ University)

Abstract

The aim of this study is to explore how self-efficacy and personality types of undergraduates affect their learning approach and to analyze the relationship between the variables involved. A model was developed using self-efficacy, personality types and learning approach and this model was tested using path analysis. The path analysis showed that extraversion, neuroticism, conscientiousness and openness had a significant effect on self-efficacy, while extroversion and openness had a significant effect on both deep and surface learning. It was further found that self-efficacy had a significant effect on deep and surface learning. According to the results, personality types directly or/and indirectly affect the learning approaches. In light of the findings of this study, when the deep learning approach is considered as the desired learning approach, it can be said that the effects of self-efficacy and personality types on deep learning were remarkable.

Suggested Citation

  • Mine Aydemir & Nuran Bayram Arlı, 2022. "A Path Analysis of Learning Approaches, Personality Types and Self-Efficacy," Contributions to Economics, in: M. Kenan Terzioğlu (ed.), Advances in Econometrics, Operational Research, Data Science and Actuarial Studies, pages 285-298, Springer.
    • Handle: RePEc:spr:conchp:978-3-030-85254-2_17
    DOI: 10.1007/978-3-030-85254-2_17
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:conchp:978-3-030-85254-2_17. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.