02 SES 03 B, Qualification Frameworks and Skills Systems
Vocational education and training (VET) is expected to equip young people and workers with skills that allow them to play an active role in the labour market (‘employability‘) or/and to access further learning. To what extent this objective is achieved is a crucial indicator of the quality of a VET system or of individual VET providers. A better understanding of the performance of VET graduates in the labour market is also one of the key sources for assessing and improving the labour market relevance of VET, alongside other methodologies like forecasts of skills supply and demand (Cedefop 2013a). Relevant information on the status of VET graduates can be obtained by ‘tracking graduates‘, i.e. through collecting quantitative and/or qualitative information about the situation of former students after completing their VET programme. Such VET graduate tracking measures may also be an important source of information for career guidance services.
The topic of VET graduate tracking has been taken up at European level and several policies and measures refer to VET graduate tracking in Member States:
- In 2009, the EQAVET Framework introduced two indicators related to VET graduate tracking;
- The 2015 Riga conclusion refers to the establishment of continuous information and feedback in VET in line with the EQAVET recommendation;
- The New Skills Agenda for Europe (2016) calls for the development of Member States’ systems for large-scale tracking of VET graduates;
- A proposal for a Council Recommendation on graduate tracking (VET and higher education) was issued in 2017.
Most European countries have established structures for collecting some kind of statistical and other data on the status of VET graduates (i.e. from labour force surveys or administrative data) or have set up more specific measures to track VET graduates. How such practices and measures are set-up is implicitly or explicitly informed by the underlying idea of the purpose of education or more specifically VET. In this regard, Hordosy 2014 distinguishes between three perspectives, namely the sociological, the humanistic and the economic - human capital theory – model. From a sociological perspective education is perceived as common good and therefore it is of interest “how the ‘group’ gaining education benefits or deteriorates the wider society”. Hence, tracking of VET graduates following a sociological model would focus on the implications of education on social mobility or social stratification (Hordosy 2014, p. 451). The humanistic model on the other hand focuses on the individual and education is perceived as becoming a fulfilled person through personal development. Tracking measures following this theoretical strand would ask about the extent and process of personal fulfilment (ibid.). Finally, the economic model seizes education as investment which “generates a stream of future benefits for the individual as higher earnings” From this viewpoint “questions around the returns of education” are central (ibid.).
However, an overview on why and how practices and measures for tracking VET graduates are established and their specific characteristics or their comparability is still missing. Furthermore, research on tracer studies often focuses on higher education (e.g. Gaebel 2012) or has a rather broad scope (e.g. secondary and tertiary education – Hordosy 2014).
Thus, this paper addresses the following questions:
- Which European countries have systematic VET graduate tracking measures established at national level?
- What are the differences and commonalities of these measures?
- What are the opportunities for making the schemes more comparable and systematic across the Member States?
For collecting, analysing, and mapping VET graduate tracking measures in EU Member States a research framework was established (e.g. based on Gaebel 2012; Hordosy 2014 and 2016). This research framework informed the development of templates for describing and analysing tracking measures. The categories used in this analysis include, for example: - the level of data collection (national, regional, local, provider level); - the methodology of data collection and the instruments used (administrative data collection, qualitative or quantitative surveys) as well as the measurement strategy (one or several measurement points, longitudinal studies); - the analysed population, the type of data collected and the indicators (e.g. related to employment or/and education); - the use and impact of the data collected (e.g. by whom, how and for what purpose are the results used). In a second step, data was collected via desk research by country researchers based on a template with mainly pre-defied answer options. When the information found through this means was scarce, country researchers contacted national authorities or training providers via e-mail or phone. In a third step, an in-depth review of selected tracking measures was conducted (case studies). This review was based on desk research and semi-structured expert interviews. The research was conducted in the context of a European study that covered all EU28 countries. The mapping focused on the national level. In the countries where VET is a decentralised responsibility, regional measures were also researched. Examples of sectoral and provider measures are also included in the mapping.
The paper presents a mapping of the measures identified in the Member States. Some findings are: - Few countries do not have any VET graduate tracking measure, others do not have a systematic approach of VET graduate tracking on national or regional level and in others available measures are not conducted on a regular basis. - From the countries which have regular measures, the focus is most often on IVET and most of them refer to employment- and education-related indicators. - Many measures use either administrative or survey data (most commonly gathered in quantitative surveys). Many countries are not taking advantage of existing administrative data for VET graduate tracking. - Survey data often relies on convenience samples and sample sizes which limit their use. - There are good examples of measures which combine data and track cohorts of VET graduates over several years to measure educational and employment outcomes. However, longitudinal measures are rarely implemented. - Only some measures track both VET graduates and drop-outs and a very limited number cover graduates who have migrated to other countries or regions. - VET graduate tracking data does not seem to be regularly and consistently used in all Member States. The results of this study suggest that the level of comparability of data collected across Member States is currently rather low. The coverage (population, types of programmes) and methodological approaches adopted (choice of time lapse between graduation and measurement, data collection tool, etc.) vary widely across measures. The highest level of commonality probably relates to the main type of data collected by VET graduate tracking measures (employment status, type of employment and participation in further education).
Cedefop (2012). From education to working life. The labour market outcomes of vocational education and training. Luxembourg: Publications Office. Cedefop (2013a). Renewing VET provision. Understanding feedback mechanisms between initial VET and the labour market. Research Paper No 37. Luxembourg, Publications Office of the European Union. Cedefop (2013b). Labour market outcomes of vocational education in Europe. Evidence from the European Union labour force survey. Luxembourg: Publications Office. Cedefop research paper; No 32. ETF/Cedefop/ILO (2016). Carrying out tracer studies. Guide to anticipating and matching skills and jobs. Volume 6. Luxembourg: Publications Office of the European Union, 2016. http://www.etf.europa.eu/web.nsf/pages/Vol._6_Tracer_studies (5/10/2017). EUROGRADUATE Consortium (DZHW, HIS, ESU, EPC) (2016): Testing the Feasibility of a European Graduate Study. Final report of the EUROGRADUATE feasibility study. http://www.eurograduate.eu/download_files/eurograduate_feasibility_report.pdf (5/10/2017). Figlio, David., Karbownik, Krzysztof., Salvanes, Kjell. (2015): Education Research and Administrative Data, in: Institute for Policy Research, Northwestern University, Working Paper Series, WP-15-13. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.720.1604&rep=rep1&type=pdf Forster, A. G., Bol, T. & Van de Werfhorst, H. G. (2016). Vocational Education and Employment over the Life Cycle. Sociological Science 3:473–94. http://thijsbol.com/wp-content/uploads/2015/09/Forster-Bol-Werfhorst-2016-SS.pdf (9/10/2017). Frawley, D. & Harvey, V. (2015). Graduate surveys. Review of international practice. Higher Education Authority (Ireland). http://hea.ie/assets/uploads/2017/06/Graduate-Surveys-Review-of-International-Practice.pdf (5/10/2017). Gaebel, M. at al. (2012). Tracking Learners‘ and Graduates‘ Progression Paths. TRACKIT. European University Association. http://www.eua.be/Libraries/publications-homepage-list/EUA_Trackit_web.pdf?sfvrsn=2 Hanushek, E. A., Schwerdt, G., Woessmann, L. & Zhang, L. (2016). General Education, Vocational Education, and Labor-Market Outcomes over the Life-Cycle. Journal of Human Resources. Hordosy, R. (2014). Who knows what school leavers and graduates are doing? Comparing information systems within Europe, in: Comparative Education, 50:4, 448-473. Hordosy, R. (2016). How do different stakeholders utilise the same data? The case of school leavers’ and graduates’ information systems in three European countries, International Journal of Research & Method in Education. Smyth, E., Gangl, M., Raffe, D., Hannan, D., and McCoy, S. (2003). A Comparative Analysis of Transitions from Education to Work in Europe (CATEWE). Final Report. https://www.researchgate.net/publication/234760045_A_Comparative_Analysis_of_Transitions_from_Education_to_Work_in_Europe_CATEWE_Final_Report_and_Annex_to_the_Final_Report (5/10/2017). UK Government Department for Business, Innovation and Skills (2013). Review of the economic benefits of training and qualifications, as shown by research based on cross-sectional and administrative data. London UK: Publications office. Research paper 105
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