Determining the number of doctoral students in the Republic of Serbia using regression algorithm

АУТОР(И): Milica Radenković

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DOI: 10.46793/TIE22.251R

САЖЕТАК:

The term data mining itself implies mining, i.e. the process of sorting, organizing or grouping a large amount of data, which enables the extraction of relevant information. More precisely, data mining leads to flexibility in data, discovery of relationships, regularity, legality and other structures where data can be organized into databases or can be textual, unstructured, derived from the Internet or data organized into time series. A significant change was made in the Bologna process with the introduction of doctoral studies, whose primary goal was to realize the link between education and research. As doctoral studies represent an important level of education, this paper is based on determining gender differences as determinants of the number of doctoral students in the Republic of Serbia. After downloading and installing the NCSS software tool, the downloading, transformation and preprocessing of data originating from the open data portal began. The result of this research is the analysis of data through regression methods, where the given regression mining technique with its set of methods made it possible to predict trends in gender differences as determinants of the number of doctors of science in the Republic of Serbia. The conducted research opens many possibilities for further research.

КЉУЧНЕ РЕЧИ: 

mining; regression; doctoral students in the Republic of Serbia; gender.

ЛИТЕРАТУРА:

• [1]D. Vilotic, R. Doroslovacki, I. Kovacevic, V.Katic, D. Seslija, S. Kolakovic, Z. Konjovic(2017). Analysis of the success of doctoralacademic studies at the Faculty of TechnicalSciences, Novi Sad. Faculty of TechnicalSciences.
• [2]O.D. Oyerinde, P.A. Chia, Predicting Students’Academic Performances. A Learning AnalyticsApproach using Multiple Linear Regression,Nigeria. University of Jos.
• [3]O. Mustapaúa, A. Karahocaa, D. Karahoca,H.Uzunboylu (2010). “Hello World”, WebMining for E-Learning, Istanbul-Turkey.University of Bahçeúehir.
• [4]Milica Radenkovic, unpublished master’sthesis. Application of web mining techniques inthe analysis of links, logos, text and opinions.Faculty of Technical Sciences in Cacak
• [5]K. A. F. Copeland (1997). Applied LinearStatistical Models. New York. McGraw-Hill/Irwin.
• [6]D. H. Barlow, V. M. Durand, S. G. Hofmann(2018). Essentials of Modern BusinessStatistics with Microsoft Office Excel, Boston.Cengage Learning.