09 SES 07 A, Regional Differences and Schooling Outcomes: Understanding seggregation
The sociocultural and socioeconomic background of pupils is determinant for their learning process (Torgyik 2015) and it also has an effect on their learning success (Oswald & Krappman 2004; Rolff, Leucht & Rösner 2008). The parents’ qualification is important as well and it is of great importance for example in the conversation and in its elaboration (Bernstein 2003) because this determinates the used vocabulary too. This knowledge has an effect on how easily the pupils can acquire the school curriculum and how they can perform on different measuring tasks. This is the reason why we compare in our paper the reading and mathematics results of the National Competence Measurement (NCM) 2013 with the English and German language results of the secondary school-leaving exams in foreign languages on middle level regarding the same pupils.
The NCM is a longitudinal measurement, the little brother of PISA and it is typical only for Hungary. All pupils of the classes of 6th, 8th and 10th grade write NCM in every spring. There are two competence fields: mathematics and reading in Hungarian. The other database gives us the results of the secondary school-leaving exams in foreign languages on middle level. There is two levels of the Hungarian school-leaving exams: middle and high. The middle level is obligatory for all pupils in Hungarian, Mathematics, History, one foreign language and one more eligible one (it is chosen by the pupils). The high level is available for pupils who would like to study in higher education. We analyse only the results of the two most popular foreign languages in Hungary (English and German) and only on middle level in our paper.
Before we present the analysis readers should know that according to the PISA results the family background has significantly bigger influence on pupils than in other countries (Arató & Varga 2004). Additionally the integration and inclusion is significantly lower than in other countries. So there is no chance for pupils with lower achievement that they can get motivation from the other pupils with better results and achievement (Csapó, Molnár & Kinyó 2009). A school has a huge role in compensating the disadvantages of pupils (Imre 2002) but in most of the cases schools cannot meet the requirements (Bourdieu 1983; Gogolin 2014).
Our research introduces the effects of pupils’ social background, the interactions of different subjects and their regional projections as well (Linberg & Wenz 2017). It has an effect on the motivation of choosing a secondary school or higher education institution and on the motivation of choosing a workplace as well (Veroszta 2010; Fekete, Hegedűs & Sebestyén 2016). The financial status of the family is important in these cases as well. The pupils/students with lower socioeconomical status choose institutions which are located near them (Denzler & Wolter 2010; Fekete, Hegedűs, Sebestyén 2016) which means that people can stay in their own regions.
We know from earlier studies and researches that the regional differences in pupils’ achievement correlate with the economical differences of regions (Bronzini & Piselli 2009, Pereira & Reis 2012). The situation is the same in Hungary (Hegedűs 2016): the best performing areas are located in the western part of Hungary; those are near to the Austrian border. The capital of Hungary and county centres have good scores as well (Garami 2009). The peripheral areas have the worst pupils’ achievement results (Garami 2014).
We analysed the database of National Competence Measurement (NCM) of 2013 and a database of secondary school-leaving exam on middle level between 2013 and 2015 with SPSS program. We analysed only the results from 10th grade because these pupils are closer to the school-leaving exam. We used only the data of pupils who could make a school-leaving exam at the end of secondary school and who had scores of the family background index, mathematical and reading competence. So we can use the results of 55156 pupils in this database. The second database contains the results of pupils who passed the school-leaving exams in English and/or German between 2013 and 2015 because we think that they were in 10th grade of secondary schools in 2013. If a pupil made more exams at this time, he/she is in the database more than one times. So we analysed the results of 49732 pupils in these databases. The same point was the regionality between the databases. We could characterize the Hungarian districts according to the results of mathematical and reading competence in NCM and English and German school-leaving exams on middle level. We analysed these data according to the family background and the school types (secondary grammar school or secondary vocational school). Firstly we calculated the average of all districts and after it we made a nonparametric test. We saw that our data divide was normal so we made a Spearman-correlation than we analysed the relations between the variables. In the second step we made clusters from the 175 regional districts of Hungary. To the clusters we used the following variables: pupils’ achievement, family background and the proportion of pupils in the secondary grammar schools and secondary vocational schools. We created 5 clusters: outstripped, going to be outstripped, “on the way”, developing, developed. We presented the results with MapInfo program and we could realise the differences between several areas of Hungary. At the end we analysed the differences between the pupils’ achievement and the socioeconomical status of the families in different types of secondary schools. We counted averages to compare the results and we used compare means in SPSS as well.
We analysed the database of NCM and English and German secondary school-leaving exam on middle level. NCM is from 2013 and the other database contains the pupils’ results who were in the 10th grade of secondary grammar schools or secondary vocational schools in 2013. We analysed the results of all areas from Hungary so we could see the territorial differences as well. There are high correlations between all of analysed variables. That means if the family background index is higher in an area than the pupils’ achievement is higher as well. Where the pupils’ achievement is higher there are well-educated parents and higher socioeconomical status of families. It is valid for the data of NCM and school-leaving exam in medium level too. It means that if in one area of Hungary a high student performance can be measured in a test then there will be higher test results in other tests in the same area as well. We divided Hungary's 175 districts into five clusters based on the pupils' achievement. The pupils' achievements show the same pattern in Hungary as the development patterns of different geographical areas in the country. The undeveloped parts of Hungary had worse results and the best results came from the well-developed areas. There is a cluster “on the way” because we don’t know yet that it will develop or its result will be getting worse. It depends on its neighbours’ districts too. The data showed that well-educated parents chose secondary grammar school for their children and the pupils of parents with lower qualifications went to secondary vocational schools. There was a big difference on the pupils’ achievement in these school types. The pupils in the secondary grammar schools had better results in the mathematical competence and in the language school-leaving exam on middle level.
Arató, F. & Varga, A. (2004): Együttműködés az együttnevelésért. Educatio, 13(3): 503-508. Bernstein, B. B. (2003): Class, codes and controll. London: Routledge. Bourdieu, P. (1983): Ökonomisches Kapitel, kulturelles Kapital, soziales Kapital. In: Kreckel, R. (ed.): »Soziale Ungleichheiten«. Göttingen. 183-198. Bronzini, R. & Piselli, P. (2009): Determinants of long-run regional productivity with geographical spillovers: The role of R&D, human capital and public infrastructure. Regional Science and Urban Economics, 39(2): 187-199. Csapó, B., Molnár, Gy. & Kinyó, L. (2009): A magyar oktatási rendszer szelektivitása a nemzetközi összehasonlító vizsgálatok eredményeinek tükrében. Iskolakultúra, 19(3-4): 3-13. Denzler, S. & Wolter, S. C. (2010): Wenn das Nächstgelegene die erste Wahl ist. Der Einfluss der geographischen Mobilität der Studierenden auf die Hochschullandschaft Schweiz. Aarau: SKBF. Fekete, A., Hegedűs, R. & Sebestyén, K. (2016): First-year English and German Language Teacher Majors’ Profile: From Where? Who? Why? and How? In: Falus, I. & Orgoványi-Gajdos, J. (eds.): New Aspects in European Teacher Education. Eger: Líceum. 115-133. Garami, E. (2009): A legkiválóbb középiskolák területi különbségei. Educatio, 18(2): 241-256. Garami, E. (2014). Kistérségi jellemzők és az oktatás eredményessége. Educatio, 23(3): 424-437. Gogolin, I. (2014): Stichwort: Entwicklung sprachlicher Fähigkeiten von Kindern und Jugendlichen im Bildungskontext. Zeitschrift für Erziehungswissenschaft, 17(3): 407-431. Hegedűs, R. (2016). A LeaRn index és a tanulói teljesítmény területi összefüggése. Educatio, 25(2): 268-277. Imre, A. (2002): Az iskolai hátrány összetevői. Educatio, 11(1): S. 63-72. Linberg, T. & Wenz, S. E. (2017): Ausmaß und Verteilung sozioökonomischer und migrationsspezifischer Ungleichheiten im Sprachstand fünfjähriger Kindergartenkinder. Journal for Educational Research Online / Journal für Bildungsforschung Online, 9(1): 77-98. Oswald, H. & Krappmann, L. (2004): Soziale Ungleichheit in der Schulklasse und Schulerfolg. Eine Untersuchung in dritten und fünften Klassen Berliner Grundschulen. Zeitschrift für Erziehungswissenschaft, 7(4): 479-496. Pereira, M. & Reis, H. (2012): What accounts for portuguese regional differences in students’ performance? Evidence from PISA. Economic Bulletin and Financial Stability Report Articles, Winter. 55-78. Rolff, H.-G., Leucht, M. & Rösner, E. (2008): Sozialer und familialer Hintergrund. In: Klieme, E. (ed.): Unterricht und Kompetenzerwerb in Deutsch und Englisch. Ergebnisse der DESI-Studie. Weinheim: Beltz. 283-300. Torgyik, J. (2015): Multikulturalizmus, interkulturális nevelés. In: Varga, A. (ed.): A neveléstudomány alapjai. Pécs: Wlislocki Henrik Szakkollégium, PTE BTK NTI Romológia és Neveléstudományi Tanszék, Romológia Kutatóközpont. 161-181. Veroszta, Zs. (2010): A munkaerő-piaci sikeresség dimenziói frissdiplomások körében. In: Garai, O., Horváth T., Kiss L., Szép L. & Veroszta Zs. (eds.): Diplomás pályakövetés IV. Frissdiplomások 2010. Budapest: Educatio Társadalmi Szolgáltató Nonprofit Kft. 11-36.
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