1. bookVolume 11 (2019): Issue 3 (September 2019)
Journal Details
License
Format
Journal
eISSN
2543-831X
First Published
25 Apr 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

Particularities of the Recent Evolution of Higher Education in Romania. Analysis and Forecasts

Published Online: 04 Dec 2019
Volume & Issue: Volume 11 (2019) - Issue 3 (September 2019)
Page range: 87 - 104
Journal Details
License
Format
Journal
eISSN
2543-831X
First Published
25 Apr 2014
Publication timeframe
4 times per year
Languages
English
Abstract

Objective: The purpose of this article is to present a brief analysis of the Romanian higher education system from the perspective of basic indicators, as well as the use of Markovian techniques for studying the evolution of the schooling process.

Methodology: The descriptive statistical analysis was mainly used to visualize and synthesize the information extracted from the data on the Romanian higher education system. Markovian methods were used to study and predict the evolution of the schooling process.

Findings: The rapid dynamics of the number of students in Romania in the last two decades has been accompanied by a series of structural changes, of which the most important are: a) constantly increasing the degree of feminisation of student achievements and b) increasing the relative importance of economic studies, legal studies and other social sciences, while reducing the relative importance of technical sciences and of medical-pharmaceutical studies within university specialties. Also, the distribution of the graduates’ specializations correlated to a very small extent with the requirements of economic and social activity. It can be said that the development of tertiary education in Romania was stimulated mainly by the action of factors of social and cultural nature and only at second level by the demand for qualified personnel generated by the productive apparatus.

Value Added: This study highlights the current state of Romanian higher education. The fact that the evolution of tertiary education has been “explosive” over the past two decades makes some econometric methods, involving the use of stationary data or which have a high degree of complexity, more difficult to use. In this context, the use of Markovian modelling methods for studying and forecasting the evolution of the schooling process can contribute to improving access to and participation in higher education.

Recommendations: In the current conjucture, when trying to increase the insertion of graduates into the labour market, it is natural for decision-makers to use various estimation methods and techniques that allow them to correlate university study programs with the needs of the labour market and at the same time provide them with scientific support for their prognosis.

Keywords

JEL Classification

Annual Report, University “Ştefan cel Mare” of Suceava (2018). www.usv.ro.Search in Google Scholar

Annual Report, University of Agronomic Sciences and Veterinary Medicine of Bucharest (2018). www.usamv.ro.Search in Google Scholar

Ashley, E. S., Guttorp, P., Anderson, J., & Caccia, D. C. (2002).A Simple Markov Chain. National Research Center for Statistics and the Environment, University of Washington.Search in Google Scholar

Declaration of the European Ministers of Vocational Education and Training, and the European Commission, convened in Copenhagen on 29 and 30 November 2002, on enhanced European cooperation in vocational education and training “The Copenhagen Declaration”. http://www.cedefop.europa.eu.Search in Google Scholar

Declaration of the Ministers in charge of vocational education and training – of EU Member States, Candidate Countries, European Economic Area Countries (2015). Riga. https://www.izm.gov.lvSearch in Google Scholar

EUROPE 2020. A strategy for smart, sustainable and inclusive growth. European Commission: Brussels, 3.3.2010 COM (2010., https://ec.europa.eu.Search in Google Scholar

European Quality Assurance in Vocational Education and Training (EQAVET) (2010). The Bruges Communiqué. https://www.eqavet.eu/Aligning-with-EQAVET.Search in Google Scholar

Eurostat statistics, www.eu.europe.eu.Search in Google Scholar

Gheţău, V. (2018). Demografia României. Bucureşti: Editura Academiei Române.Search in Google Scholar

Iosifescu, M., Grigorescu, S., Oprişan, Gh., & Popescu, Gh. (1984).Elemente de modelare stohastică. Bucureşti: Editura Tehnică.Search in Google Scholar

Klock, F., & Nimmer, J. (2001). Markov Models, ESP High School Studies Program Lecture 10, M-20A: Square Peg Solutions August.Search in Google Scholar

Law no.1 / 2011, National Education, Art. 117. https://www.edu.ro.Search in Google Scholar

National System of Indicators for Education. Methodological Guide, Bucharest, 2014, www.edu.ro.Search in Google Scholar

PISA for Development Assessment and Analytical Framework, Reading, Mathematics and Science. PISA results 2017. www.oecd.org.Search in Google Scholar

Ratitch, B., & Precup, D. (2001).Characterizing Markov Decision Processes. Montreal, Canada: McGill University.Search in Google Scholar

Report on the state of higher education in Romania 2016–2017. Ministry of National Education. https://www.edu.ro.Search in Google Scholar

Report on the state of higher education in Romania 2017–2018, Ministry of National Education. https://www.edu.ro.Search in Google Scholar

Statistical notebooks on Higher Education, National Institute of Statistics (NIS), 2012–2018.Search in Google Scholar

Stefǎnescu, St. (2000).Numerical Analysis. Bucharest: University of Bucharest Publishing House.Search in Google Scholar

TEMPO-online database of the National Institute of Statistics. www.insse.ro.Search in Google Scholar

White, D. J. (1993).Markov Decision Processes. Willey John W. &Sons.Search in Google Scholar

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