36 5. 1. 1 Universities ranked in QS University ranking...36 5. 1. 2 Academic ranking of a Computer science Faculty...
38 5. 1. 3 Employer Ranking of a Computer science Faculty...40 5. 1. 4 Citations Ranking of a Computer science Faculty...
42 5. 1. 5 R&d Expenditures by ICT Firms...44 5. 1. 6 ICT FP7 Funding to Private Organisations...
46 5. 1. 7 ICT FP7 Participations...48 5. 1. 8 ICT FP7 Funding to SMES...
131 8. 1 QS WORLD UNIVERSITY RANKINGS by QS...131 8. 2 ICT FP7 by EC DG Connect...
Munchen Kreisfreie Stadt EIPE ID card Activity Characteristic Name of Indicator Indicator ID Rank R&d Agglomeration Universities ranked in the QS University ranking Agrd 1
32 Academic ranking of a Computer science faculty Agrd 2 10 Employer ranking of a Computer science faculty Agrd 3 11 Citations ranking of a Computer science faculty Agrd 4 29 R&d
Inner London East EIPE ID card Activity Characteristic Name of Indicator Indicator ID Rank R&d Agglomeration Universities ranked in the QS University ranking Agrd 1
18 Academic ranking of a Computer science faculty Agrd 2 7 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 6 R&d
Paris EIPE ID card Activity Characteristic Name of Indicator Indicator ID Rank R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 37 Academic ranking
of a Computer science faculty Agrd 2 8 Employer ranking of a Computer science faculty Agrd 3 8 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT
the EIPE ID card Activity Characteristic Name of Indicator Indicator ID Nr R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 1 Academic ranking of a Computer science
faculty Agrd 2 2 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT firms
R&d 5. 1. 1 Universities ranked in QS University ranking Table 13: Top ranking regions according to the Universities ranked in QS University ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKL12 Gwynedd 100
266 2 DE711 Darmstadt, Kreisfreie Stadt 83 7 3 DE125 Heidelberg, Stadtkreis 82 23 4 DE423 Potsdam, Kreisfreie Stadt 77
UKH12 Cambridgeshire CC 20 5 Indicator description Indicator ID Agrd 1 Name of indicator Universities ranked in the QS University ranking
Measures the number of universities in QS university ranking based in a region Unit of measurement Region's share in the total number of EU ranked universities to a region's share in the EU population Definition of ICT dimension none Unit of observation
NUTS 3 Source QS WORLD UNIVERSITY RANKINGS by QS (see Section 8. 1) Reference year (s) considered 2011 37 Figure 22:
Frequency of the Universities ranked in QS University ranking indicator values 1248 9 8 8 7 4 3 6 1 1 2 1 1 1
2 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Number of universities ranked in QS Table 14:
Descriptive statistics of the Universities ranked in QS University ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 16 7. 01 49.17 38 5. 1. 2
Academic ranking of a Computer science Faculty Table 15: Top ranking regions according to the Academic Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKH12
Cambridgeshire CC 100 5 2 UKJ14 Oxfordshire 87 19 3 UKI22 Outer London-South 73 114 4 UKM25 Edinburgh, City
UKE21 York 27 63 29 ITD55 Bologna 27 76 Indicator description Indicator ID Agrd 2 Name of indicator Academic ranking of a Computer science faculty
Measures the performance of the Computer science faculty according to the academic ranking of QS Unit of measurement The highest rank of a Computer science faculty in the academic ranking Definition of ICT dimension Computer science faculty Unit of observation NUTS
3 Source QS WORLD UNIVERSITY RANKINGS by QS (see Section 8. 1) Reference year (s) considered 2011 39 Figure 23:
Frequency of the Academic Computer science faculty QS Ranking indicator values 1244 1 5 5 12 11 8 9 2 2 1 1
1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Academic ranking of a Computer science faculty Table 16:
Descriptive statistics of the Academic Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 38 7. 25 52.59 40 5
. 1. 3 Employer Ranking of a Computer science Faculty Table 17: Top ranking regions according to the Employer Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKH12
Cambridgeshire CC 100 5 2 UKJ14 Oxfordshire 95 19 3 UKI12 Inner London-East 68 2 4 UKI22 Outer London
30 GR300 Attiki 28 49 Indicator description Indicator ID Agrd 3 Name of indicator Employer ranking of a Computer science faculty What does it measure?
Measures the performance of the Computer science faculty according to the employer ranking of QS Unit of measurement The highest rank of a Computer science faculty in the employer ranking Definition of ICT dimension Computer science faculty Unit
of observation NUTS 3 Source QS WORLD UNIVERSITY RANKINGS by QS (see Section 8. 1) Reference year (s) considered 2011 41 Figure 24:
Frequency of the Employer ranking of a Computer science faculty indicator values 1244 1 3 3 12 12 11 6 6 1 1 1 2 0
500 1000 1500 Frequency 0 20 40 60 80 100 Employer ranking of a Computer science faculty Table 18:
Descriptive statistics of Employer Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 47 7. 63 58.27 42 5
. 1. 4 Citations Ranking of a Computer science Faculty Table 19: Top ranking regions according to the Citations Computer science faculty QS Ranking indicator Rank NUTS3 Code Region name Indicator Value EIPE Rank 1 UKL12
Gwynedd 100 266 2 PL127 Miasto Warszawa 91 50 3 NL335 Groot-Rijnmond 77 72 4 FR101 Paris 75 3
Gent 37 94 Indicator description Indicator ID Agrd 4 Name of indicator Citations ranking of a Computer science faculty What does it measure?
Measures the performance of the Computer science faculty according to the citations ranking of QS Unit of measurement The highest rank of a Computer science faculty in the citations ranking Definition of ICT dimension Computer science faculty Unit
of observation NUTS 3 Source QS WORLD UNIVERSITY RANKINGS by QS (see Section 8. 1) Reference year (s) considered 2011 43 Figure 25:
Frequency of the Citations Computer science faculty QS Ranking indicator values 1243 3 11 10 9 6 7 3 2 3 2 2
1 1 0 500 1000 1500 Frequency 0 20 40 60 80 100 Citations ranking of a Computer science faculty Table 20:
Descriptive statistics of Citations Computer science faculty QS Ranking indicator Number of observations Mean value Standard deviation Variance 1303 1. 94 9. 57 91.58 44 5
of indicator Universities ranked in the QS University ranking Academic ranking of a Computer science faculty Employer ranking of a Computer science faculty Citations ranking of a Computer science faculty R&d expenditures by ICT firms ICT FP7 funding
Measures the number of universities in QS university ranking Measures the performance of the Computer science faculty according to the academic ranking of QS Measures the performance of the Computer science faculty according to the employer ranking of QS Measures the performance
of the Computer science faculty according to the citations ranking of QS Measures the average annual amount spent on R&d in the ICT sector Measures the amount received for research in ICT R&d Unit of measurement Region's share in the total
number of EU ranked universities to a region's share in the EU population The highest rank of a Computer science faculty in the academic ranking The highest rank of a Computer science faculty in the employer ranking The highest rank of a Computer science
faculty in citations ranking Region's share in the R&d expenditures by ICT firms in the EU to a region's share in the EU population Region's share in the total EU ICT FP7 funding to a region's share in the EU population Definition of ICT dimension None Computer science faculty Based on NACE
Rev. 2 ICT areas of the FP7 programme Unit of observation NUTS 3 Source QS WORLD UNIVERSITY RANKINGS by QS (Section 8. 1) Company level information:
Orbis by Bureau Van dijk (Section 8. 7) ICT FP7 by EC DG CONNECT (see Section 8. 2) Reference year 2011 2005-2011 2007-2011
Data Sources 8. 1 QS WORLD UNIVERSITY RANKINGS by QS The Computer science and Electronic Faculties rankings originate from the QS WORLD UNIVERSITY RANKINGS,
which was formed in 2008 to meet the increasing public interest for comparative data on universities and organisations,
and the growing demand for institutions to develop deeper insight into their competitive environment. 4 The QS WORLD UNIVERSITY RANKINGS currently considers over 2,
000 and evaluates over 700 universities in the world, ranking the top 400. Like any ranking at the global level, it is constrained by the availability of data from every part of its scope.
To construct measures of faculty performance, the QS uses its proprietary datasets that enable to drill down by subject area, namely academic and employer reputation surveys and the Scopus data for the Citations per Faculty indicator in the global rankings.
These have been combined to produce the results. In detail each of the faculty ranking pieces can be described in the following way:
Academic Reputation survey is the centrepiece of the QS WORLD UNIVERSITY RANKINGS since their inception in 2004. In 2010, it drew upon over 15,000 respondents to compile the results.
In the survey, respondents are asked to identify the countries, regions and faculty areas that they have most familiarity with
and up to two narrower subject disciplines in which they consider themselves expert. For EACH of the (up to five) faculty areas they identify,
respondents are asked to list up to ten domestic and thirty international institutions that they consider excellent for research in the given area.
Employer reputation survey considers the students'employability as a key factor in the evaluation of international universities and in 2010 drew on over 5,
The employer survey works on a similar basis to the academic one only without the channelling for different faculty areas.
Citations per Faculty takes into account the size of an institution while allowing observing its penetration the global research landscape.
When aggregated together these totals per faculty and their associated citations provide an indicator of volume and quality of output in the given discipline.
Aggregation, similarly to the approach used in the overall QS WORLD UNIVERSITY RANKINGS a z-score is calculated for each indicator with the results scaled between 0 and 100
32 5. 1 QS WORLD UNIVERSITY RANKINGS by QS...32 5. 2 FP7 database by EC DG Connect...
In particular, the EIPE project builds up a measurement of ICT R&d activity by observing the actual presence of ICT technology producers (universities, companies, R&d facilities), their R&d expenditures and bibliometric data.
the EIPE project builds up this measurement by observing the level of agglomeration of technology producers (universities, companies, R&d facilities), R&d expenditures and bibliometric data.
o Computer science and engineering with respect to university faculties, o Computer science with respect to scientific publications, o ICT hardware and software with respect to R&d activity performed in R&d centres,
FP7 data on FP participation from EC DG Connect, REGPAT by OECD, QS WORLD UNIVERSITY RANKINGS by QS, Web of Science by Thomson Reuters, Design Activity Tool by IHS isuppli, European
the EIPE ID card Activity Characteristic Name of Indicator Indicator ID Nr R&d Agglomeration Universities ranked in the QS University ranking Agrd 1 1 Academic ranking of a Computer science
faculty Agrd 2 2 Employer ranking of a Computer science faculty Agrd 3 3 Citations ranking of a Computer science faculty Agrd 4 4 R&d expenditures by ICT firms
In particular, they acknowledge the importance given in EIPE to the presence and the quality of major knowledge production organisations, such as universities (and their computer science departments), private and public research centres (in particular those of multinational companies), innovative SMES
of indicator Universities ranked in the QS University ranking Academic ranking of a Computer science faculty Employer ranking of a Computer science faculty Citations ranking of a Computer science faculty R&d expenditures by ICT firms FP7 funding
Measures the number of universities in QS university ranking Measures the performance of the Computer science faculty according to the academic ranking of QS Measures the performance of the Computer science faculty according to the employer ranking of QS Measures the performance
of the Computer science faculty according to the citations ranking of QS Measures the average annual amount spent on R&d in the ICT sector Measures the amount received for research in ICT R&d Unit of measurement Region's share in the total
number of EU ranked universities to a region's share in the EU population The highest rank of a Computer science faculty in the academic ranking The highest rank of a Computer science faculty in the employer ranking The highest rank of a Computer science
faculty in citations ranking Region's share in the R&d expenditures by ICT firms in the EU to a region's share in the EU population Region's share in the total EU FP7 funding to a region's share in the EU population Definition of ICT dimension None Computer science faculty Based on NACE Rev
. 2 (see Table 1) ICT areas of the FP7 programme (see Section 5. 2) Unit of observation NUTS 3 Source QS WORLD UNIVERSITY RANKINGS by QS (see Section 5. 1
) Company-level information: ORBIS by Bureau Van dijk (see Section 5. 7) FP7 database by EC DG Connect (see Section 5. 2) Reference year (s) considered 2011 2005-2011 2007-2011 10
The performance of universities and computer science faculties across the world, as reported by the QS University ranking.
For a detailed description of the data source, see Section 5. 1. Information about the funding
1. QS WORLD UNIVERSITY RANKINGS by QS, 2. FP7 database by EC DG Connect, 3. Bibliometrics: Web of Science by Thomson Reuters, 4. ICT R&d centres locations:
each of the data source is described. 5. 1 QS WORLD UNIVERSITY RANKINGS by QS The rankings of Universities
and Computer science and Electronic Faculties originate from the QS WORLD UNIVERSITY RANKINGS. It was formed in 2008 to meet the increasing public interest in comparative data on universities and organisations,
and the growing demand for institutions to develop deeper insight into their competitive environment. 16 The QS WORLD UNIVERSITY RANKINGS currently considers over 2,
000 universities in the world and evaluates over 700 of them, ranking the top 400.
This list is used to build an indicator of the location of a ranked university in a region within the current project.
In addition due to the fact the QS ranking includes 52 subject disciplines, one of which is Computer science,
additional faculty-level information is extracted for the purpose of the EIPE study. To construct measures of faculty performance,
the EIPE study used QS proprietary datasets to investigate its subject area at three levels, namely academic and employer reputation surveys and the Scopus data for the Citations per Faculty indicator.
In detail, each of the faculty ranking pieces can be described in the following way: The Academic reputation survey is the centrepiece of the QS WORLD UNIVERSITY RANKINGS since their inception in 2004.
In 2010, it drew upon over 15,000 respondents to compile the results. In the survey, respondents are asked to identify the countries,
regions and faculty areas that they have most familiarity with and up to two narrower subject disciplines in
which they consider themselves expert. For each of the faculty areas they identify, respondents are asked to list up to ten domestic
and thirty international institutions that they consider excellent for research in the given area. They are not able to select their own institution.
The Employer reputation survey considers the students'employability as a key factor in the evaluation of international universities and in 2010 drew on over 5,
The employer survey works on a similar basis to the academic one only without the channelling for different faculty areas.
Citations per faculty takes into account the size of an institution, and also observes its penetration into the global research landscape.
When aggregated, these totals per faculty and their associated citations provide an indicator of volume and quality of output in the given discipline.
in addition to the university ranking, it also offers the rankings described above by teaching subject, including Computer science.
The main constraint is that it offers only a limited number of universities, which does not allow us to cover the entire population of the European higher education institutions. 5. 2 FP7 database by EC DG Connect The Framework Programmes for Research and Technological Development,
also called Framework Programmes or abbreviated to FP1, through to FP7, are funding programmes created by the European union
University of Technology. Ottaviano, G, . & Thisse, J.-F. 2004). Agglomeration and economic geography. In J. V. Henderson, P. Nijkamp, E s. Mills, P. C. Cheshire & J. F. Thisse (Eds.
Printed in Spain 1 A Policymakers Guide to Transnational Learning in Smart Specialisation Åge Mariussen*,Inger Midtkandal**and Ruslan Rakhmatullin***Botnia-Atlantica Institute, University of Vaasa (Finland
), Umeå University (Sweden), Nordland Research Institute (Norway)**European commission, JRC-IPTS, Seville (Spain) S3 Policy Brief Series n°05/2014 January
and just skip any thought of adapting universities to regional development needs. These and other obstacles to triple helix connectivity might be frustrating,
if you are able to create new university industry relations, or repair other flaws in your system of innovation,
such as the triple helix of universities and public policies does not support the industry and its suppliers sufficiently,
why is not this university generating more spin-offs? In seeking the explanations to this question,
you should look at spin-offs from other universities. Is there something wrong with the organization of the science park?
Or perhaps there is a lack of connectivity between the universities and industrial actors who has a potential to commercialize university-based innovations?
What about the absorptive capacity of your industries? Problems often have root causes, chains of causation
A common problem is that the university institutions have strengths in terms of research which is matched not by regional industries.
which are technologically related to the strengths of the University. With no industrial actors willing and able to industrialize university-based innovations
patents from your university are likely to be sold and industrialized in other regions. There might be ways around these structural problems,
providing you discover and use appropriate policies. In this discovery process, there is a lot to learn from the history of other more successful regions you identified in the first place.
Why are their industry better connected to regional universities? In many cases what appears to be a structural problem today is the result of policy decisions made some years ago.
For instance, in comparing connectivity between universities and industries between Nordic countries, we have found that an explanation to the excellent contacts between industries
and universities in Finland is due to certain reforms in the Finnish school system which created open doors from craft based education to poly-technical university-level education.
This is a good practice. The hypothesis is that it can be transferred to other regions in other countries through a reform of your existing educational institutions
Other good practices is the frequent contacts between some Norwegian universities and tourism is that the universities tries to support innovation in tourism through innovation programs,
in synergy with programs encouraging 11 tourist entrepreneurs to cooperate in promoting destinations. These are short term strategies,
Smart specialisation, regional growth and applications to EU Cohesion Policy, Economic geography Working Paper, Faculty of Spatial Sciences, University of Groningen:
Academy of Sciences, higher education institutions and research institutes, national and regional organisations and market players with an RDI involvement.
Csongrád and Hajdú-Bihar counties because of academic research in their universities the number of researchers is high. 7393 4120 Budapest 14125 capita (FTE) Apart from Budapest 8894 capita
and discussions about the further development and mainstreaming of ICT-ELI were conducted (list of participants is in the Annex 2). The authors are grateful to Professor Nancy Law (University of Hong kong) for organizing the expert workshop in Hong kong,
Ola Erstad (University of Oslo), Paul Kelley (Science+Technology in Learning), Marco Kools (OECD-CERI), Anne Looney (Irish National Council for Curriculum and Assessment
developed by researchers at Department of Social Informatics and methodology at the Faculty of social science, University of Ljubljana, Slovenia.
) 12 8. 1 Decision-makers (e g. school head, chief education officer, university dean, etc. 16 10.7 Technology providers/developers 7 4. 7 Others 14 9. 4 Total 149 100.0 The vast majority of the respondents were from 22 European countries
teacher trainer researcher policy-maker (at EU, national, regional, local level) decision-maker (e g. school head, chief education officer, university dean, etc.
Name Affiliation Stefania Bocconi National Research Council of Italy Barbara Brecko JRC-IPTS Roberto Carneiro Portuguese Catholic University, Portugal Miroslava Cernochova
Charles University in Prague, Czech republic Jonatan Castaño-Muñoz JRC-IPTS Anusca Ferrari JRC-IPTS Conor Galvin University college Dublin, Ireland Seungyeon Han
Hanyang Cyber University, South korea Kampei Hayashi Japan Society for the Promotion of Science Panagiotis Kampylis JRC-IPTS Paul Kelley Science+Technology in Learning, United kingdom
Marco Kools CERI-OECD Carmen Lazaro Ítaca School, Spain Nancy Law University of Hong kong Chee-Kit Looi National Institute of Education
Yves Punie JRC-IPTS Magdalena Sverc Institute Anton Martin Slomsek, Slovenia Christine Redecker JRC-IPTS Tamotsu Tokunaga University of Tsukuba, Japan
Keith Turvey University of Brighton, United kingdom Stella Vosniadou National and Kapodistrian University of Athens, Greece Riina Vuorikari E-learning expert, Belgium 43 Participants in the'Scaling
of Education, Singapore Kai Ming Cheng University of Hong kong Seungyeon Han Hanyang Cyber University, South korea Ronghuai Huang Beijing Normal University, China Dae
Joon Hwang Korean Council for University Education, South korea Yu Kameoka Ministry of Education, Culture, Sports, Science and Technology, Japan Panagiotis Kampylis JRC-IPTS
Gwang-Jo Kim, UNESCO Bangkok Nancy Law University of Hong kong Chee-Kit Looi National Institute of Education
Singapore Jingyan Lu University of Hong kong Naomi Miyake University of Tokyo, Japan Jonghwi Park UNESCO Bangkok Yves Punie JRC-IPTS Mang She Education
Capability Maturity Model The Capability Maturity Model (CMM)( 4) was defined originally for software development by Carnegie mellon University
Version 1. 1. Pittsburgh, PA, Carnegie mellon University, 2002.5. Spewak SH & Hill SC. Enterprise Architecture planning-developing a blueprint for data applications and technology.
and hosted by the Institute for Triple Helix Innovation based at the University of Hawaii at Manoa in the United states of america. 13 A draft questionnaire was developed
SMES, research centres and universities. Once organised, these consortiums can participate in the programme by answering calls for proposals
1. Help for SMES and SME associations for outsourcing their research activities to providers of research services i e. universities or research centres.
No. of Phd Graduates in the Midwest Region 2008-2010 University of Limerick Mary Immaculate College Limerick Institute of technology 2008-2009 102 5 1 2009-2010
Currently 28%of graduates from the University of Limerick are being retained in the Midwest Region.
despite having no University 24 in the Region (there are a number of other Higher Level Institutes),
& Innovation Resources in the Midwest Region Chapter 04 4. 1 Higher education Institutes 4. 1. 1 University of Limerick Research Strengths The University of Limerick (UL
) recognises the mutual benefit that flows from engagement between the Midwest Region and the University's research goals.
Research Institutes represent the highest level of research infrastructure within the University, and provide focused support for research, both within faculties and across faculties.
University of Limerick currently has four research institutes, namely: The Materials and Surface Science Institute (MSSI),
which undertakes research focused on the design of materials for health, transport, energy and clean technology;
while a new Clinical Education & Research Facility at the University Hospital Limerick campus will provide for the necessary education
is a collaboration between the University of Limerick, Limerick IT, IT Tralee and Mary Immaculate College.
Technology Centres are based in a University with support from partner colleges to deliver on the research needs of the companies.
Two Research Centres are hosted by the University of Limerick: The irish Centre for Composites Research and The Pharmaceutical Manufacturing Technology Centre.
The University also participates in a number of other research centres. Limerick Institute of technology is involved current in the International Energy Research Centre.
Collaboration between Industry and Higher education Institutes The Enterprise Research Centre in the University of Limerick has worked in collaboration with industry on a number of research initiatives.
In 2013 the Alliance managed a yearlong‘Innovation Dublin Festival'to promote open innovation by businesses, Higher education institutions and communities.
It brings together people from businesses, universities, research, finance and technology organisations to stimulate innovation through knowledge transfer.
The Technology Transfer Office in the University of Limerick plays a key role in this area in the Midwest Region.
University of Limerick and Limerick Institute of technology have a key role to play. It is recommend that they continue to engage with other HEIS, private industry,
Enterprise Ireland NEXUS Innovation Centre Limerick Institute of technology Enterprise Research Centre Cook Medical University of Limerick Technology Transfer Office Vistakon IDA Ireland
Helpful inputs and suggestions were received from Joan Calzada Aymerich from the University of Barcelona (Chapter 4), Jake Kendall from the Gates Foundation, Anoush Tatevossian and Alex Rutherford from UN Global Pulse,
as well as the groups in the public sector, nonprofit, university and private in order to answer the needs of community Open Data Definition Open data means that data should be freely available to everyone to use
%and the Euro Area average of 8. 2%.As regards the implementation of E-learning solutions in universities,
in 2010 more than half of the universities (58%)had E-learning solutions, and in the years that followed other 9 universities have implemented such platforms by means of POSCCE and POSDRU financing.
Page 70 of 170 3. 1. 4 3. 1. 5 Strategic Lines of Development Strategic Lines of Development for ICT in Education in Romania Strategic Lines
women 83 71 53 57 University 50 50 39 37 Of which: women 29 29 24 23 1at the end of the year. 2source:
by means of governmental programmes and several other means The majority of the pupils in the urban environment have a satisfactory level of digital alphabetisation A great number of universities have already been equipped with e-learning platforms (more than 70
and the improvement of the access to technology irrespective of the provisions of the academic environment The computerisation of the libraries and the formation of digital competencies in the rural environment leading towards The decrease of the scholastic population in the pre-university teaching
between universities The students and the teachers'mobility The lack of collaboration between the business environment and the educational institutions The lack of correlation between the e-learning programmes included in the Sectoral Operational Programme Increase of economic competitiveness (SOP IEC) with the e-learning
universities, SMM, regulatory forum The introduction of fiscal facilities for the companies which invest in the research, development and innovation activity The technological advantage of the developed states from an economical point of view The increase competency
Romanian Office for Adoptions Page 146 of 170 Enrolment in education Primary school/high school/university Primary school 1. Enrolling based on the identity documents of parents
based on the means of the 8th grade exam, depending o preference University 1. Submission of Registration 2. Payment of the examination fee (if applicable) 3. Examination 4. Result
the high school graduation diploma is submitted in original at the faculty chosen 4. 2 In case of failure,
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