09 SES 07 B, Investigating Carrier Pathways and Dropout
In the Hungarian school system, economic, social and cultural status (ESCS) of the family strongly affects students learning possibilities even on the secondary level of schooling. The type of the secondary school – grammar school, vocational grammar school or vocational school without a matura examination and with no direct route to the university – and the “goodness” of the secondary school as measured by the abilities of its students and the probability of entering higher education after finishing it strongly depends on family background.
PISA studies found that Hungarian school types differ not only in the cognitive abilities of their students but in their ESCS-profile as well. Index of social inclusion (the percent of variation in students’ ESCS coming from differences within schools) is 62.6 percent (S.E. 2.40) in Hungary compared to the OECD-average 76.8 percent (S.E. 0.38). Amongst the OECD countries, similarly low or lower values were found only in Chile, Mexico, the Slovak Republic and Spain. Besides the ESCS-index of students and schools explains 80.1 percent of the differences in performance on the PISA science ability scale between schools, which differences between schools are considerably higher than the OECD average: 56.6 percent compared to 30.1 percent of the average total variation in science performance across OECD countries.
Recent longitudinal research programs showed that differences in students’ performance between Hungarian schools and classes are growing from lower primary to upper primary to secondary schools. However, our knowledge about the factors and processes leading to the established social stratification for the age of secondary schooling is limited. Therefore, our research project aims to link Grade 4 characteristics of the representative sample of Hungarian students participating in the TIMSS & PIRLS 2011 international large-scale student assessment with their data from later grades up to Grade 10 in the spring of 2017.
Our research questions are the following. (a) What percent of students did not reach Grade 10 in six years from 2011 and what are their characteristics in Grade 4 in terms of family background and performance. (b) What percent of students did fall out completely from the Hungarian school system by the spring of 2017 and what are their characteristics? (c) What are the features making a student vulnerable for drop out and what school and teacher characteristics can have a role in mitigating these effects? (d) What is the connection between Grade 4 characteristics of students and their schools and the type and quality of their later secondary school?
We are using data from three sources: TIMSS & PIRLS 2011 joint international database; joint databases of the National Assessment of Basic Competencies (National ABC) from 2012 to 2017; and the Public Education Information System of Hungary. Student level data from different sources were linked by the student measurement ID, which is a unique ID used in every large-scale student assessment in Hungary from 2008. TIMSS&PIRLS 2011 data provides detailed information about student performances in reading, science and mathematics, about their family background along with their schools’ and teachers’ characteristics. National ABC measures performances in reading and mathematics in Grades 6, 8 and 10 also along with some, but fewer background and school characteristics. Data from the Public Education Information System was used to identify TIMSS & PIRLS 2011 students’ grade and school on 1st June 2017. To answer our research questions we are going to use descriptive statistics, linear and logistic regression models. To ensure that results are valid for the whole Hungarian student population, we apply TIMSS & PIRLS 2011 weights and the jackknife error calculating methods during our analysis. We use SPSS along with the IEA IDB Analyzer software. First, we are going to give a description of students with different school career (dropped out, lagging behind, regular progress, in different school types) in regard their family background and achievement in Grade 4. Then, we are going to test whether different characteristics of students, schools and teachers have an effect on drop out or on the quality of the secondary school after controlling for family background and achievement. Characteristics to investigate are students’ engagement in, self-confidence in and attitude toward reading, mathematics and science, schools’ emphasis of academic success, school climate and discipline, teachers’ confidence in teaching, teacher collaboration practices or teachers’ instruction practices to engage students.
Linking of databases using the measurement ID was successful for 99.5 percent of TIMSS&PIRLS 2011 students. From the student population of Grade 4 in the 2010-11 school year, 7.9 percent dropped out from the Hungarian school system by the end of 2016-17 school year, 1.2 percent was still in primary schools, 91 percent started their secondary school, and 76.2 percent were in their second year of secondary schooling. From students reaching the secondary school level, 42.6 percent learned in grammar schools, 35.2 percent in secondary vocational schools and 22.2 percent in vocational schools. Our preliminary analysis showed that students who later dropped out from the Hungarian school system on average had similar levels of home resources for learning and achievement in Grade 4 to students who were still in primary school at the end of the 2016-17 school year. They had somewhat lower levels of home resources for learning and somewhat lower average achievement in Grade 4 in mathematics and science than students continuing their studies in vocational schools, while in reading no significant differences were found between the two groups of students. In accordance with our expectations, students with low achievement in Grade 4 had higher dropout rate and were less likely to study in grammar or secondary vocational schools later than the average student was. For students not reaching the low international benchmark in at least one assessment domain the dropout rate was 25.5 percent, and only 21.1 percent followed their studies in grammar or vocational grammar schools. In contrast, 95.5 percent of students reaching the advanced international benchmark at least in one domain studied in these two school types six years later. Using multivariate regression methods we hope to explore variables affecting school career of students on which educational policies and school practices have an influence.
Csapó Benő (2014): A szegedi iskolai longitudinális program. In: Pál József és Vajda Zoltán (szerk.): Szegedi Egyetemi Tudástár 7. Bölcsészet- és társadalomtudományok. Szegedi Egyetemi Kiadó, Szeged. 117-166. Martin, M. O., Mullis, I. V. S. (Eds., 2013)., TIMSS and PIRLS 2011: Relationships Among Reading, Mathematics, and Science Achievement at the Fourth Grade—Implications for Early Learning. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College. Martin, M. O., Mullis, I. V. S., Foy, P., Stanco, G. M. (2012), TIMSS 2011 International Results in Science. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College. Mullis, I. V. S., Martin, M. O., Foy, P., Arora, A. (2012), TIMSS 2011 International Results in Mathematics. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College. Mullis, I. V. S., Martin, M. O., Foy, P., Drucker, K. T. (2012), PIRLS 2011 International Results in Reading. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College. OECD (2016), PISA 2015 Results (Volume I): Excellence and Equity in Education. PISA, OECD Publishing, Paris. http://dx.doi.org/10.1787/9789264266490-en Ostorics L., Szalay B., Szepesi I., Vadász Cs. (2016). PISA2015 Összefoglaló jelentés. Oktatási Hivatal, Budapest. Tóth E., Csapó B., Székely L. (2010). Az iskolák és osztályok közötti különbségek alakulása a magyar iskolarendszerben Egy longitudinális vizsgálat eredményei. Közgazdasági Szemle, LVII. évf., 2010. szeptember (798–814. o.)
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