The Republic of North Macedonia's Research Ranking Platform for Academic Staff and Universities

Arbnor Rushiti, Artan Luma, Ylber Januzaj, Azir Aliu, Halil Snopçe, Anis Sefidanoski, Mitko Trpkoski

© 2024 Arbnor Rushiti, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International. (CC BY-NC 4.0).

Citation Information: SAR Journal. Volume 7, Issue 1, Pages 3-11, ISSN 2619-9955,, March 2024.

Received: 08 January 2024.
Revised:   14 February 2024.
Accepted: 19 February 2024.
Published: 27 March 2024.


In the present-day realm of higher education, the integration of open data and machine learning has become a crucial means of improving the evaluation and ranking of research publications by university professors. This research endeavours to address the pressing need for an effective computerized paradigm that combines the collection, analysis, and use of public data in higher education. The main objective is to provide recommendations in the form of informative reports for interested institutions and to rank university professors' scientific works on an open data platform. This research not only contributes to the advancement of educational evaluation methodologies but also responds to the growing importance of open data and machine learning in modern higher education. By automating the assessment of research publications, universities can better track and enhance their academic impact, ultimately fostering a culture of excellence in research and innovation.

Keywords – higher education, ranking, big data, machine learning, open data.


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