The LeaRn Project 2011-2016: Main results and Lessons to Learn

Author(s):

Tamás Kozma(presenting / submitting)Katalin R Forray

Conference:

ECER 2017

Network:

15. Research Partnerships in Education

Format:

Paper

Session Information

15 SES 06, Case Study (Part 1)

Paper Session to be continued in 15 SES 07

Time:

2017-08-23

15:30-17:00

Room:

K6.16

Chair:

Magali Hardouin

Contribution

The LeaRn Project (Learning Regions in Hungary, 2010--2016) has been based on two theoretical backgrounds (Kozma 2016): first, a consideration of the new dimensions of learning (learning as a social activity); second, a consideration of the spatial distribution of learning as a social activity. The aim of the project was to analyse the existing territorial units of the country (various habitats, towns, urban centres etc.) on the basis of their learning activities, and then, using the data collected, to describe types of territories in terms of their learning activities. The main aim was to explore and ascertain the spatial distribution of learning in the country, that is, to identify the learning regions in Hungary.

The first results of the LeaRn Project have been presented in the ECER conferences between 2012-2015 (ECER 2012; 2013; 2014; 2015). Here a concluding report about the main results of the project would be presented, together with some considerations of the lessons to learn.

The LeaRn Project has some antecedents, including various endeavours to evaluate ‘the spatial structure of social learning’ (Erdei et al, 2011). The LeaRn Project was modelled on the Canadian Composite Learning Index (Canadian Council of Learning 2010) and the German Atlas of Learning (Schoof et al 2011).

The Canadian learning index. The philosophy of Canada’s Composite Learning Index (CLI) went back to the Delors Report. In line with the 1996 UNESCO Report on Lifelong Learning (Delors et al 1996), the CLI also had four ‘pillars’. The four pillars of the Delors Report had been operationalised with a view to ensuring that the lifelong learning progress could be statistically monitored. The pillars had been measured using indicators (17) as part of the statistical opreationalising process, with the indicators then being evaluated by 26 measures. At the end of the process, each of the country’s territorial units was given a score based on the CLI. In this way, a comparison of Canada’s territorial units could be made in terms of annual learning (education) progress or stagnation.

The European ‘lifelong learning index’. The ELLI index (ELLI: European Lifelong Learning Indicators, see Hoskins et al 2010) has been initiated by the Bertelsmann Foundation. The original idea was an adaptation of the Canadian CLI (this is also the origin of the acronym) with the idea of characterising member states of the European Union on the basis of their learning processes, just like the Canadian provinces.

The German ‘Atlas of lifelong learning’ (Deutscher LernAtlas, DLA, Schoof et al 2011). It represented a follow-up and more elaborate version of ELLI. Its philosophy was the same, but the published results were far more elaborate. Further, the DLA reflected a situation that was much closer to the Central European one than to the Canadian forerunner. While the Canadian data collection represents a model of regional statistical research and the analysis of lifelong learning statistics, the DLA constitutes a model of the operationalisation of the four pillars, the essential basis for all current empirical data gathering on the topic of lifelong learning. Turning to the LeaRn Project, these two--the Canadian CLA and the German DLA--were the closest models followed in the creation and analysis of the ‘LeaRn Index’ of Hungary.

The Hungarian ‘LeaRn index’. The Hungarian ‘LeaRn index’ (HLI) has been based on the earlier two indices (CLI, ELLI). We applied indicators of formal learning (Pillar I), non-formal learning (Pillar II), community learning (Pillar III) and cultural learning (Pillar IV). (See Kozma 2016: 25)

Method

A ‘learning region’ has been defined by the LeaRn Project as an objective for territorial development. Therefore It had to be operationalised (dimensions, pillars) and assessed (values for measurement). Dimensions. Based on the background literature, the LR was operationalised in four dimensions (the four ‘pillars’). Pillars III and IV represented community engagements and personal enrichments in the original documents. Dimension A consists of the existing infrastructure of formal learning in a given territorial unit. Dimension B covers the non-formal learning settings. Dimension C means the ‘learning potential’ of an area under investigation. Dimension D is the political dimension. Two sub-dimensions of dimension D can be differentiated: top-down and bottom-up political actions for a growing LR. Indicators of the four dimensions of the HLI were, then, formulated and their statistical values collected. (the measures). Dimension A comprised the statistical indicators of formal educational organisations. Dimension B has been measured using indicators of adult professional education as well as continuing VET activities. Dimension C has been operationalised as ‘cultural learning’ and has been measured with the help of leisure-time statistics. Dimension D has been understood as civic and political engagement and measured with the help of existing statistics of NGOs, activities in the political elections, and religion-based processes as well as other existing statistics of volunteering. Regional units were the habitats in Hungary. Although it is not sufficient for a direct analysis of the regions, they may suffice for an investigation of emerging learning communities. It was expected, based on the theoretical backgrounds (see the earlier section of the present chapter), that regional analysis would indicate clusters of learning communities where these communities would create territorial clusters and would, therefore, produce regions. Statistical sources were the institutional and census data of the Central Statistical Office of Hungary. Statistical analyses have been conducted by descriptive as well as multi-dimensional methods. The results of the regional analysis in those areas where communities created clusters showed, therefore, the emergences of types of learning regions. Case studies. The function of the case studies was to provide knowledge and understanding of the political actions and processes that might or might not lead to the emergence of learning communities and regions.

Expected Outcomes

Results. Three different types of LR’s in the country have been found. Learning regions. Budapest, the capital, together with its urban area emerge as the leading LR of Hungary. It might even be called the only LR of the country. In a more inclusive interpretation, three additional could also be called the LRs of the country. They are starting from Budapest and leading to the Western, to the South Western and the South Eastern part of Hungary. Learning cities. Further parts of the country emerge as smaller or larger individual territories. (Debrecen, Miskolc, Szolnok and to a lesser extent, also Pécs). They are typical urban centres with their vicinities. Learning communities. The emergence of ‘learning communities’ are also visible. They are communities--mostly separated from one or the other ‘learning cities’--still showing signs of economic, political and cultural activities. Lessons. The concepts of ‘learning region’ proved to be a powerful tool to understand the socio-economic processes of the given social and territorial units, and to contribute to theirs further developments. The explanation lies in the various human resources of the given territorial unit and they networking and partnership activities. The LeaRn Project--following its predecessors, the Canadian CLI, the European ELLI and the German Atlas of Learning--proved that a multidimensional vision of human learning--cultural and community learning besides the formal and non-formal learning--may lead to a better understanding and a more embraced vision of the human development of the territory where we live.

References

Bandura A (1977). Social Learning Theory. Oxford etc: Prentice-Hall. Benke M ed (2014) Learning Regions: A Special Issue. Hungarian Educational Research Journal 4 (2014), 3. DOI:10.14413/HERJ2014.03.01 Delors J. et al. (1996). Learning: The Treasure Within. Paris: UNESCO. ECER 2012: From Lifelong Learning to Learning Regions. (See ECER 2012 programme booklet) ECER 2013: VET School or Learning Centre? (See ECER 2013 programme booklet) ECER 2014: Learning Communities in the Making (See ECER 2014 programme booklet) ECER 2015: The Role of Learning in Political Change (see ECER 2015 programme booklet) Erdei G et al (2012) “Lifelong learning in a cross-border setting.” In: Pusztai G, Hatos A eds (2012) Higher Education for Regional Social Cohesion. Debrecen: Center for Higher Education R&D, 163-180. Faure E et al (1972), Learning to Be: The world of education today and tomorrow. Paris: UNESCO. Forray, R K, Kozma, T (2011), The School in the Space and Time. Budapest: Új Mandátum Kiadó (Hungarian) Hoskins B et al 2010 ELLI Index Europe. Gütersloh: Bertelsmann Stiftung. (German) http://www.deutscher-lernatlas.de/fileadmin/Inhalte/Informationen/DLA/ELLI_EU_dt_final.pdf (Accessed: 14.01.2017) Kozma T (2014), “The learning region; A critical interpretation.” Hungarian Educational Research Journal 4 (2014) 3. DOI:10.14413/HERJ2014.03.05 Kozma T. ed (2016) Learning Regions in Hungary: From Theory to Reality. Brno: Tribun EU Publishers Osborne M (2014), “Learning cities 2020.” Hungarian Educational Research Journal 4 (2014), 3. DOI 10.14413/HERJ2014.03.02 Rutten R, Boekema F eds (2007) The Learning Region. Cheltenham: Edward Elgar Publishers Schoof U et al (2011), German Atlas of Learning: Findings 2011. Gütersloh: Bertelsmann Stiftung (German). https://www.bertelsmann-stiftung.de/de/publikationen/publikation/did/deutscher-lernatlas-ergebnisbericht-2011/ (Accessed: 14.01.2017) UNESCO (2017), The Composite Learning Index and European Lifelong Learning Indicators. http://www.unesco.org/new/en/education/themes/strengthening-education-systems/quality-framework/technical-notes/composite-learning-index/ (Accessed 14.01.2017)

Author Information

Tamás Kozma (presenting / submitting)

University of Debrecen

CHERD (Center for HE R&D)

Debrecen

Katalin R Forray

University of Pécs

Roma Studies

Pecs

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