IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i24p16335-d995996.html
   My bibliography  Save this article

The Interest Level Assessment in Attending Training Programs among Romanian Teachers: Econometric Approach

Author

Listed:
  • Silviu Nate

    (Department of International Relations, Political Science and Security Studies, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania)

  • Daniel Mara

    (Department of International Relations, Political Science and Security Studies, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania)

  • Alin Croitoru

    (Department of International Relations, Political Science and Security Studies, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania)

  • Felicia Morândau

    (Department of International Relations, Political Science and Security Studies, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania)

  • Andriy Stavytskyy

    (Department of Economic Cybernetics, Faculty of Economics, Taras Shevchenko National University of Kyiv, 01601 Kyiv, Ukraine)

  • Ganna Kharlamova

    (Department of Economic Cybernetics, Faculty of Economics, Taras Shevchenko National University of Kyiv, 01601 Kyiv, Ukraine)

Abstract

The article explores the determinants of Romanian in-service teachers’ willingness to participate in a national training program focused on mentoring in education. A multidimensional analytical model and survey data collected from a large sample of Romanian teachers (N > 5000) revealed a specific profile of those teachers who are interested in joining this type of training in education. It is found that individuals’ interest in joining the training program is positively affected by a higher level of education, prior experiences of attending training programs, and higher awareness of the role of mentoring in education. At the same time, individuals’ self-assessed needs for training and more challenges faced in online/blended teaching during the pandemic period also increase the teachers’ chances to be interested in joining the training program. However, a negative relationship is found between age and the willingness to enroll in the training program. Based on these general findings, the article advances the comparisons between three subsamples of teachers depending on their teaching level (primary education, lower-secondary education, and upper-secondary education). The study is designed to contribute to the general debate on reforming education systems through mentoring in education, and its findings can inform policymakers and stakeholders in the field.

Suggested Citation

  • Silviu Nate & Daniel Mara & Alin Croitoru & Felicia Morândau & Andriy Stavytskyy & Ganna Kharlamova, 2022. "The Interest Level Assessment in Attending Training Programs among Romanian Teachers: Econometric Approach," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16335-:d:995996
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/24/16335/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/24/16335/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jason S. Bergtold & Elizabeth A. Yeager & Allen M. Featherstone, 2018. "Inferences from logistic regression models in the presence of small samples, rare events, nonlinearity, and multicollinearity with observational data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(3), pages 528-546, February.
    2. Eleftherios K. Soleas & Mary A. Code, 2020. "Practice Teaching to Teaching Practice: An Autoethnography of Early Autonomy and Relatedness in New Teachers," SAGE Open, , vol. 10(2), pages 21582440209, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jean Philippe Décieux & Alexandra Mergener, 2021. "German Labor Emigration in Times of Technological Change: Occupational Characteristics and Geographical Patterns," Sustainability, MDPI, vol. 13(3), pages 1-18, January.
    2. Pan, Yan & Jing, Yunteng & Wu, Tonghai & Kong, Xiangxing, 2022. "Knowledge-based data augmentation of small samples for oil condition prediction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Yalta, A. Talha & Yalta, A. Yasemin, 2018. "Are credit rating agencies regionally biased?," Economic Systems, Elsevier, vol. 42(4), pages 682-694.
    4. Yang Zhao & Beomsoo Kim, 2022. "Environmental Regulation and Chronic Conditions: Evidence from China’s Air Pollution Prevention and Control Action Plan," IJERPH, MDPI, vol. 19(19), pages 1-17, October.

    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:jsusta:v:14:y:2022:i:24:p:16335-:d:995996. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.