24 SES 07 B JS, The Role of Language and Family Characteristics for Mathematics and Science Achievement: Family characteristics and educational aspirations
Joint Paper Session NW 09, NW 14 and NW 24
Contextual factors of student's home and family background characteristics on educational achievement are well researched. Since the Coleman’s report in 1966 (Hanushek, 2010) out of school determinants of students’ achievement have been extensively researched (Sirin, 2005). Hattie (2008) in his synthesis of meta-analyses relating to achievement, reports the effect size associated with socioeconomic status (SES) of d=0.57. This fairly large effect size serves as a warning that any valid interpretations about students’ achievement should account for student’s background. This is also evident in research surrounding Programme for international student assessment (PISA), where contextual factors are routinely applied to research equity and quality of education (OECD, 2016).
Students’ performance differences according to family wealth in Slovenia are small (OECD, 2017; 417) and score difference in performance between children of white-collar vs blue –collar workers is average (OECD, 2017; 421). Contrary to that is Slovenia among countries with the highest enrolment difference in general or vocational upper secondary programmes by SES profile (OECD, 2016; 169) and has high index of social segregation between general and vocational schools (OECD, 2017; 423). This research explores relationship of SES with selection of upper secondary educational track among students in Slovenia. More narrowly, it focuses on relationship of SES and mathematics achievement for 14-year-old students with selection of most demanding general educational track named “Gymnasium”. Research is a part of greater efforts to implement socio economic data in system-wide feedback to schools that is happening in Slovenian upper secondary schools through an interactive software solution for giving back the information on students' achievement in schools (i.e. teacher's grade) and on external examinations of General and Vocational Matura. It is named Assessment of/for Learning Analytic Tool or shortly ALAT (Zupanc, Urank & Bren, 2009; Urank, Zupanc & Cankar, 2012).
In past years, we demonstrated the needs for inclusion of contextual factors into ALAT if we want to facilitate sound and meaningful interpretation (Cankar, Bren & Zupanc, 2017). More importantly, we demonstrated that high quality SES data could be obtained from administrative sources of Statistical Office of Republic of Slovenia (SORS) and thus avoiding the need for expensive data gathering that could have serious problems of validity (Sirin, 2005). By demonstrating large effect of SES on further school career in two subsequent populations of students this research provides previously unpublished findings and stresses the need to systematically and continually monitor SES and thus provide means to address emerging issues with evidence based decisions.
We will research correlations of SES index and mathematics achievement with student’s selection of most demanding general educational track (“Gymnasium”) on two subsequent cohorts of Slovenian students, using national census data from Slovenian Statistical Office and achievement data from National examinations Centre. We will replicate analyses on PISA 2015 data to demonstrate validity and generalizability of findings.
Our research questions could be framed as: (1) What is the size of correlations of socio economic status data and mathematics achievement data with student’s selection of most demanding upper secondary general educational track and, (2) what are practical implications of such association in terms of probability in selecting Gymnasium given certain level of socio economic index and prior mathematics achievement.
Methods This research will be conducted through a research contract with Slovenian national statistics agency (Statistical Office of the Republic of Slovenia - SORS), where we will join the data from national assessments at the end of 9th Grade (and data about selection of upper secondary schools) with the national register of households. Through this we will link students with their parents living in same household and through further connections with databases of income (year 2011), real-estate (2011), working population data (2011) and general census data (2011) for all their parents. This multi database link will be established to calculate socio economic index (SEI). Main part of this research involves linked data from NEC and SORS: 1. National assessment results for Grade 9 populations of 2009 and 2010 –Maths achievement – census data obtained from National Examinations Centre (NEC). 2. External examination results on General and Vocational Matura (NEC) 3. National register of households (SORS) 4. Population census 2011 (SORS) 5. Real estate register (ownership, estimated value) 2011 (SORS) 6. Register of working population 2013 (SORS) 7. Income tax register 2011 (SORS) Rest of the data is PISA 2015 database, where we will create parallel analyses.
Expected results Results from research will show the extent to which SES and mathematical achievement relate to student’s selection of Gymnasium both in terms of correlations and in terms of probabilities of selection given certain levels of both factors. We will be able for example to demonstrate which of both factors has greater impact and if factor is negligible. Comparison of results from national census data with PISA 2015 results will in case of similar findings increase validity of inferences from both datasets and provide an argument for using the administrative data in evidence based decision-making. Results can serve as an example of valid systematic approach to educational improvement as noted by Slavin (2002) where high stakes decisions are based on evidence and driven by appropriate data. This could lead to development of long term educational governance as defined by Coward (2010).
Cankar, G., Bren, M. in Zupanc, D. (2017). Za večjo pravičnost šolskega sistema v Sloveniji [Promoting greater equity in Slovenian educational system], Ljubljana: Državni izpitni center. Coward, R. (2010). Educational governance in the NHS: A literature review. International Journal of Health Care Quality Assurance, 23(8), 708–17. Hanushek, E. A. (2010). How well do we understand achievement gaps? Focus, 27(2), 5–12. Hattie, J. (2008). Visible learning : a synthesis of meta-analyses relating to achievement. London : New York: Routledge. OECD (2016), PISA 2015 Results (Volume I): Excellence and Equity in Education, PISA, OECD Publishing, Paris. OECD (2017), PISA 2015 Results (Volume III): Students’ Well-Being, PISA, OECD Publishing, Paris. Sirin, S. R. (2005) Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research. Review of Educational Research. 75(3), 417-453. Slavin, R. E. (2002). Evidence-Based education policies: Transforming educational practice and research. Educational Researcher, 31(7), 15–21. Zupanc, D, Urank, M. & Bren, M. (2009). Variability analysis for effectiveness and improvement in classrooms and schools in upper secondary education in Slovenia: assessment of/for learning analytic tool. School Effectiveness and School Improvement, 20(1): 89–122. Urank, M., Zupanc, D. & Cankar G. (2012). Orodje za analizo izkazanega znanja ob zaključku srednje šole. [Assessment of/for Learning Analytic Tool]
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