IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p4780-d1090939.html
   My bibliography  Save this article

Collaborative Learning Supported by Blockchain Technology as a Model for Improving the Educational Process

Author

Listed:
  • Goran Bjelobaba

    (Department for e-Business, Faculty of Organizational Sciences, University of Belgrade, 11000 Belgrade, Serbia)

  • Ana Savić

    (School of Electrical and Computer Engineering, Academy of Technical and Art Applied Studies, 11000 Belgrade, Serbia)

  • Teodora Tošić

    (Faculty of Applied Management, Economics and Finance, University Business Academy, 21107 Novi Sad, Serbia)

  • Ivana Stefanović

    (School of Electrical and Computer Engineering, Academy of Technical and Art Applied Studies, 11000 Belgrade, Serbia)

  • Bojan Kocić

    (Department of Business Studies Blace, Toplica Academy Professional Studies, 18420 Blace, Serbia)

Abstract

After COVID-19, new accreditation standards include the need for developing better learning and teaching environments. This will be supported and connected with digitization, entrepreneurship, social inclusion, and a circular economy. The orientation towards equity and quality in education clearly imposes the need for an individual approach to each student separately. This situation is especially pronounced in higher education institutions in the field of technology, whose primary goal is very often individual training for use of highly specialized software and hardware tools. In such a situation, it is necessary to move away from the classical ex-cathedra methodology and develop student-centered learning environments. Global accreditation systems for teaching, learning, practice, and business communication can be simplified using blockchain. On the basis of blockchain technology (BCTs), this paper proposes a Collaborative Learning and Student Work Evaluation (CLSW) model that includes a multi-frontal teaching method (VFN) and combines scientific peer-review standards. BCTs are used to protect student project and assessment data storage and transmission. Assisting higher education institutions in finding “employable capabilities” of proactive students is the idea of CLSW. Before implementing the CLSW paradigm, a poll of lecturers’ views on BCTs was conducted. The poll results show a desire and willingness to teach with BCTs. The model’s fundamental capabilities and the key participants’ duties were described in a project framework. Additionally, this research and proposed model can improve educational process sustainability in general, as it is an open platform easily accessible by all the interested parties, thus contributing to life-long learning.

Suggested Citation

  • Goran Bjelobaba & Ana Savić & Teodora Tošić & Ivana Stefanović & Bojan Kocić, 2023. "Collaborative Learning Supported by Blockchain Technology as a Model for Improving the Educational Process," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4780-:d:1090939
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/4780/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/6/4780/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fang Liu & Wei-dong Zhu & Yu-wang Chen & Dong-ling Xu & Jian-bo Yang, 2017. "Evaluation, ranking and selection of R&D projects by multiple experts: an evidential reasoning rule based approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1501-1519, 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. Goran Bjelobaba & Marija Paunovic & Ana Savic & Hana Stefanovic & Jelena Doganjic & Zivanka Miladinovic Bogavac, 2022. "Blockchain Technologies and Digitalization in Function of Student Work Evaluation," Sustainability, MDPI, vol. 14(9), pages 1-22, April.
    2. Zhexuan Zhou & Yajie Dou & Jianbin Sun & Jiang Jiang & Yuejin Tan, 2017. "Sustainable Production Line Evaluation Based on Evidential Reasoning," Sustainability, MDPI, vol. 9(10), pages 1-14, October.
    3. Zhu, Weidong & Zhang, Tianjiao & Wu, Yong & Li, Shaorong & Li, Zhimin, 2022. "Research on optimization of an enterprise financial risk early warning method based on the DS-RF model," International Review of Financial Analysis, Elsevier, vol. 81(C).
    4. Weidong Zhu & Shaorong Li & Hongtao Zhang & Tianjiao Zhang & Zhimin Li, 2022. "Evaluation of scientific research projects on the basis of evidential reasoning approach under the perspective of expert reliability," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 275-298, January.

    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:15:y:2023:i:6:p:4780-:d:1090939. 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.