Information reconstruction in educational institutions data from the European tertiary education registry

Universities and other organizations providing higher level education are collectively called Higher Education Institutions. Their detail data, for instance number of students, number of graduates, etc., constitute the basis for several important analyses of the educational systems. This work provides data of the European Tertiary Education Register (ETER), which describes the Educational Institutions of Europe. These data have been gathered through the National Statistical Authorities of all the Countries participant in the ETER Project. However, they include many scattered missing values. Therefore, we have developed and applied an imputation methodology (see “Imputation Techniques for the Reconstruction of Missing Interconnected Data from Higher Educational Institutions, Bruni et al. [3]) to replace the missing values with feasible values being as similar as possible to the original values that have been lost and are now unknown. Thus, we also provide the imputed version of the same dataset, which allows more in-depth analyses of the European Higher Education Institutions. Both datasets (before and after imputation) are provided in two versions: with or without bibliometric information for the Institutions, so the user can also consider these additional information if interested

A Tailor-made Data Quality Approach for Higher Educational Data

This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register (ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain. The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information. The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated. The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives. The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions

Student mobility in tertiary education: institutional factors and regional attractiveness

Member States have committed themselves to promoting the learning mobility of young people following the 2011 Communication on an agenda for the modernisation of Europe’s higher education system (COM(2011) 567). The Council conclusions on a benchmark for learning mobility (2011/C 372/08) specified that by 2020 ‘an EU average of at least 20% of higher education graduates should have had a period of higher education-related study or training abroad’. In this report, two types of mobility are distinguished, namely degree mobility and credit mobility, both of which are included in the benchmark. Little research has been carried out on international student mobility determinants in general and on Erasmus students in particular, especially taking into account the regional dimension of learning mobility. This report focuses on student mobility in the EU between 2011 and 2014, through the description of the main destinations of mobile students, as well as on inward mobility across and within countries (measured as the share of mobile students on total student population), with a particular focus on institutions and regions. It also analyses the main factors associated with degree and credit mobility, taking into account different tertiary education levels (i.e. undergraduate, master and PhD level), through the comparison between institutional factors (teaching and research activities of universities as well as their reputation) and regional attractiveness (level of urbanisation, employment opportunities and regional education systems). There are five main conclusions from this report. First, in relation to the most attractive destinations, degree mobility appears to be very concentrated in a few countries, while credit mobility tends to be more equally distributed across Member States. Second, degree mobility is higher than credit mobility across and within countries. Third, institutional characteristics tend to be associated with student mobility more than regional ones. Fourth, among institutional characteristics, better quality universities and those with a higher reputation are associated with a higher share of mobile students, while research orientation and excellence are more relevant for degree mobile PhD students. Fifth, among regional characteristics, the level of urbanisation of the region is an important factor in shaping students’ mobility: high-density regions have higher degree mobility rates, but a lower share of credit mobile students.JRC.B.4-Human Capital and Employmen

Integration in the European higher education area: the case of military education

Military training has always been considered as an education system with its own characteristics that distinguished it from the rest of higher education. However, different initiatives have been developed in order to integrate military education in the European Higher Education Area (EHEA). This paper analyses the European system of military institutions of higher education (MHEI). Results indicate MHEI sector has distinctive features that have increased the diversity of European HEIs. Further, the emergence of the MHEI sector can have benefits for both the European defence and their educational attainment. From a defence point of view, it can help the development of a European strategic culture and increase cooperation between countries in defence and security. From the education area modernisation, it will improve defence and security related research and may enhance defence knowledge transfer. In this way, the MHEI sector will be positioned as key player in the development of the Common Security and Defence Policy and a European strategic culture

Imputation techniques for the reconstruction of missing interconnected data from higher Educational Institutions

Educational Institutions data constitute the basis for several important analyses on the educational systems; however they often contain not negligible shares of missing values, for several reasons. We consider in this work the relevant case of the European Tertiary Education Register (ETER), describing the Educational Institutions of Europe. The presence of missing values prevents the full exploitation of this database, since several types of analyses that could be performed are currently impracticable. The imputation of artificial data, reconstructed with the aim of being statistically equivalent to the (unknown) missing data, would allow to overcome these problems. A main complication in the imputation of this type of data is given by the correlations that exist among all the variables. We propose several imputation techniques designed to deal with the different types of missing values appearing in these interconnected data. We use these techniques to impute the database. Moreover, we evaluate the accuracy of the proposed approach by artificially introducing missing data, by imputing them, and by comparing imputed and original values. Results show that the information reconstruction does not introduce statistically significant changes in the data and that the imputed values are close enough to the original values

Higher education system rankings and benchmarking

The authors were involved in the Benchmarking Higher Education System Performance project of the OECD referred in the text. However, the opinions expressed in this chapter are those of the authors and do not necessarily reflect the views of the OECD and of its members.The purpose of this chapter is to discuss the emergence of higher education system rankings and other frameworks that attempt to make sense of the performance of higher education systems. It starts with a review of higher education system rankings and how they attempt to overcome the failings of institutional rankings. It then covers alternative approaches for monitoring higher education beyond traditional rankings. It introduces the approach of benchmarking higher education system performance rooted in the literature on performance, the performance of public services, and the performance of higher education. It offers a view of what is possible to do with an ontological approach to the performance of higher education systems instead of exercises driven by data availability and discusses the challenges of moving forward with such an approach. It concludes by discussing the likely coexistence of the discourses on world-class university with the world-class systems, and the challenge for countries to balance them. © Ellen Hazelkorn and Georgiana Mihut 2021.(undefined

Comparing university performance by legal status: a Malmquist-type index approach for the case of the Spanish higher education system

This is an Accepted Manuscript of an article published by Taylor & Francis in Tertiary Education and Management on 2017, available online: http://www.tandfonline.com/10.1080/13583883.2017.1296966New public management and increasing levels of competition driven by global rankings are bringing the managerial practices of public and private higher education institutions closer together. However, these two types of institutions still maintain different objectives and traditions and enjoy different degrees of autonomy that are reflected in their internal organisational structures. We study the relative efficiency and productivity performance of private and public universities in Spain through two adaptations of the Malmquist Index. Results show that, in 2009/2010, the greater flexibility of private universities meant a better adjustment between inputs and outputs in the private sector. However, in 2013/2014, public universities had caught up with private universities. Because of the economic crisis, the inputs of public universities have decreased, but this decrease had not fully impacted their results in 2013/201

Determinants of the incidence of non-academic staff in European and US HEIs

In this article, we contribute to the scant literature covering quantitative studies on the determinants of the non-academic staff incidence in higher education institutions by analysing how the proportion of non-academic staff is related to key features such as size, prestige, year of foundation and financial structure of universities. We apply nonlinear regression analysis to compare HEIs across Europe and the USA, taking into account time and cross-country heterogeneity of the two balanced panel datasets concerning European and American universities over a period of 6 years (2011–2016 for Europe and 2012–2017 for the USA). Evidence suggests that in both Europe and the USA, public and larger (if sufficiently large) as well as more research-oriented units are characterised by a higher proportion of non-academic staff. In Europe, we observe an inverted U-shaped effect of the share of non-personnel expenditure and the foundation year on the proportion of non-academic staff, while the proportion of non-academic staff decreases with the share of core and third-party funding. For the USA, we obtain similar findings except that the share of core funding and third-party funding is characterised by a U-shaped effect, and the impact of the share of non-personnel expenditure has no empirical effect on the proportion of non-academic staff. Additionally, we discover that some factors that contribute to the proportion of non-academic staff may constitute indicators of performance, suggesting the need for further research to extend our knowledge on the complex issue of the role played by non-academic staff in university performance