RESEARCH IN VET IN SEE – CHALLENGES AND LESSONS LEARNED Jasminka Čekić Marković Centre for Education policy ERISEE Interim Agency Regional Workshop: Quality Development in VET – from Check to Act November, 2012

2 Research in VET – general remarks  VET is not dominant thematic scope  In general studies on education the specifics of VET have been neglected or only particular aspects have been studied (i.e. connection with the labour market)  The perspectives of important stakeholders as often been neglected  Evidence providers - units within ministries and government institutions, different research institutes and organisations, non-governmental organisations  International institutions/organisations as evidence providers

3 Research in VET – general remarks Joint initiatives  Torino process – reinforcement of evidence-based policy making  SEE VET net - exchange of experience, development of comparable data bases, training in gradute tracer studies  “Teachers for the future ” – CEP/ETF regional project based on analysis of policies and practices of teacher development for inclusive education in the Western Balkan region against the European trends and the latest international research in the field

4 Research in VET – general remarks  International researches and the ones conducted by international organisations are considered more reliable  Decision makers have no clear vision of how relationship with research community should look like  The existing relationship and interaction is not based on formal institutional links, but rather on ad hoc or random communication and cooperation, and sometimes of informal nature  The interaction is in principle reactive or problem driven, rather than proactive or strategic  The lack of feedback information on the use of research in the process of education policy formulation

5 Experience – data collection  The matter of human resources in institutions responsible for VET as well as in schools  Action researches are not represented in school practices  Systematic data are usually missing, unavailable or insufficient  Data are usually collected through short-term projects or initiatives. They are partial, related to one field of research only.  In the case of complex phenomena researches, it requires the data from several research areas which is not always attainable

6 Experiences – data collection  The problem of data relevance – inappropriate categorisation and classification of data  The problem of longitudinal research – if the data are collected and organised in fragments, the monitoring of different processes is more complicating and often impossible. This is particularly problematic in education where is necessary to include a long-term perspective, to predict possible causes of certain problems as well as to monitor the effectiveness of measures introduced  Data are usually quantitative - it is often impossible to understand causes and reasons for some phenomena if we rely only on quantitative data. If this is so, we risk an inadequate identification of strategic objectives / problems, and later on adoption of inappropriate policy measures.

7 Experiences – data analysis and interpretation  Unsystematic data collection – the researches focus on individual problems and levels of education  Lack of multidisciplinary approaches  Excessive focus on the micro-level (learning, interaction in the classroom)  Insufficient focus on the meso-level (the school) or micro-level (system level)  Methodology used by researchers are diverse and often unreliable, and the findings are presented inappropriately  Research important for education and adoption of policy measures are not financed from the budget sufficiently

8 Recommendations General recommendations  Institutionalise cooperation between researches and decision makers  Provide feedback about the use of researches in defining of education policy in the VET sector...regarding data collection  Make database “operational”, complete and suitable for the current context  Provide qualitative data...regarding data analysis  Data integration and cooperation  Work on a multidisciplinary approach and focus on all levels (micro, meso, macro)  Investigate the relations between levels

9. Thank you for your attention!!!