Kosovar Students’ Performance in the 2019 TIMSS Assessment: Why School and Home Resources Drive Achievement?

Abstract

Results of the TIMSS 2019 reveal that Kosovar students have lower achievement levels in both mathematics and science. In understanding this gap in achievement, it is of value to take a closer look at factors that may influence student learning. Results of this analysis reveal that factors such as home and school resources influence achievement with students who report more home resources and attend high-resource schools outperforming other groups of students. Student perceptions of their learning also shaped achievement with students performing higher when they had higher levels of confidence and preference for the subjects.

Keywords

home resources school resources student achievement Kosovo

Get full access to this article

View all access options for this article.

References

Agirdag

Van Houtte

Van Avermaet

(2012). Why does the ethnic and socio-economic composition of schools influence math achievement? The role of sense of futility and futility culture. European Sociological Review. https://doi.org/10.1093/esr/jcq070

Akiba

LeTendre

G. K.

Scribner

J. P.

(2007a). Teacher quality, opportunity gap, and national achievement in 46 countries. Educational Researcher, 36(7), 369–387. https://doi.org/10.3102/0013189X07308739

Akiba

LeTendre

G. K.

Scribner

J. P.

(2007b). Teacher quality, opportunity gap, and national achievement in 46 countries. Educational Researcher, 36(7), 369–387. https://doi.org/https://timss.bc.edu/timss2015-advanced/downloads/TA15_FW_Ref.pdf

Bishop

Wossmann

(2004). Institutional effects in a simple model of educational production. Education Economics, 12(1), 17–38. https://doi.org/10.1080/0964529042000193934

Bourdieu

(1986). The forms of capital. In Richardson

(Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). Greenwood. https://www.socialcapitalgateway.org/content/paper/bourdieu-p-1986-forms-capital-richardson-j-handbook-theory-and-research-sociology-educ

Bradley

R. H.

Corwyn

R. F.

(2002). Socioeconomic status and child development. Annual Review of Psychology, 53(1), 371–399. https://doi.org/10.1146/annurev.psych.53.100901.135233

Brese

Mirazchiyski

(2013). Measuring students’ family background in large-scale international education studies. Issues and methodologies in large-scale assessments. Special issue 2. IERI Monograph series. IERI. Retrieved from http://www.ierinstitute.org/fileadmin/Documents/IERI_Monograph/Special_Issue_2/10_IERI_Special_Issue_2_complete.pdf

Broer

Bai

Fonseca

(2019). Socioeconomic inequality and educational outcomes. International association for the evaluation of educational achievement (IEA). Springer Cham: Springer.

Broer

Xie

Bohrnstedt

G. W.

(2017, April). Construction and Validation of a New Proxy Socioeconomic Status Measure for NAEP Grade 12. In. J. H. Tang (Chair), The Development and Applications of Alternative Student Socioeconomic Status Measures. Symposium conducted at the meeting of the American Educational Research Association, San Antonio, TX. https://static1.squarespace.com/static/51bb74b8e4b0139570ddf020/t/5c191bff4d7a9cc4a7886c9f/1545149440519/Session+5+-+Holmes_Broer.pdf

10.

Chiong

Shuler

(2010). Learning: Is there an app for that? Investigations of young children’s usage and learning with mobile devices and apps. The Joan Ganz Cooney Center at Sesame Workshop. https://clalliance.org/wp-content/uploads/files/learningapps_final_110410.pdf

11.

Chiu

M. M.

Khoo

(2005). A new method for analyzing sequential processes: Dynamic multilevel analysis. Small Group Research, 36(5), 600–631. https://doi.org/10.1177/1046496405279309

12.

Clerkin

(2018). Pre-school numeracy play as a predictor of children’s attitudes towards mathematics at age 10. Journal of Early Childhood Research, 16(3), 319–334. https://doi.org/10.1177/1476718X18762238

13.

Coleman

J. S.

(1988). Social capital in the creation of human capital. American Journal of Sociology, 94(Supplement), S95–S120. https://doi.org/10.1086/228943

14.

Coleman

J. S.

(1990). Foundations of social theory. Cambridge: Harvard University Press.

15.

Dahl

G. B.

Lochner

(2005). The impact of family income on child achievement. National Bureau of Economic Research Working Paper 11279.

16.

Davis-Kean

P. E.

(2005). The influence of parent education and family income on child achievement: The indirect role of parental expectations and the home environment. Journal of Family Psychology, 19(2), 294–304. https://doi.org/10.1037/0893-3200.19.2.294

17.

Ferreira

F. H. G.

Gignoux

(2011). The measurement of inequality of opportunity: Theory and an application to Latin America. Review of Income and Wealth, 57, 622–657. https://doi.org/10.1111/j.1475-4991.2011.00467.x

18.

Gutnick

Robb

Takeuchi

Kotler

(2011). Always connected: The new digital media habits of young children. The Joan Ganz Cooney Center at Sesame Workshop. https://joanganzcooneycenter.org/publication/always-connected-the-new-digital-media-habits-of-young-children/

19.

Hoy

W. H.

(2002). Faculty trust: A key to student achievement. Journal of School Public Relations, 23. https://doi.org/10.3138/jspr.23.2.88

20.

Koršňáková

Meinck

Japelj Pavešić

(2022). Introduction to Dinaric Perspectives on TIMSS 2019. In Japelj Pavešić

Koršňáková

Meinck

(Eds.), Dinaric perspectives on TIMSS 2019. IEA Research for Education (Vol. 13). Cham: Springer, Cham. https://doi.org/10.1007/978-3-030-85802-5_1

21.

Lareau

(2011). Unequal childhoods: Class, race, and family life (2nd ed., with an update a decade later.). University of California Press. https://www.ucpress.edu/book/9780520271425/unequal-childhoods

22.

Lee

V. E.

Burkam

D. T.

(2002). Inequality at the starting gate: Social background differences in achievement as children begin school. Economic Policy Institute, 1660 L Street, NW, Suite 1200, Washington, DC 20036. http://www.sciepub.com/reference/193972

23.

Lieberman

D. A.

Bates

C. H.

(2009). Young children's learning with digital media. Computers in the Schools, 26(4), 271–283. https://doi.org/10.1080/07380560903360194

24.

Mullis

I. V. S.

Martin

M. O.

Foy

Hooper

. (2016). International association for the evaluation of educational achievement (IEA) PIRS 2016 international results in reading. Retrieved from https://eric.ed.gov/?id=ED580353

25.

OECD (2006). Education policies for students at risk and those with disabilities in South Eastern Europe. OECD. https://doi.org/10.1787/9789264036161-en

26.

Ogut

G. B.

Broer

(2016). Paper presentation `Investigating SES using the NAEP-HSLS overlap sample” at the United States’. Washington, DC: National Council on Measurement in Education (NCME).

27.

Rideout

Foehr

U. G.

Roberts

D. F.

(2010). Generation M ²: Media in the lives of 8- to 18- year-olds. Henry J. Kaiser Family Foundation. https://files.eric.ed.gov/fulltext/ED527859.pdf

28.

Rumberger

R. W.

Palardy

G. J.

(2005a). Does segregation still matter? The impact of social composition on academic achievement in high school. Teachers College Record, 107, 1999–2045. https://eric.ed.gov/?id=EJ718513

29.

Schneider

Jürges

Büchel

(2005). The effect of central exit examinations on student achievement: Quasiexperimental evidence from TIMSS Germany. Journal of the European Economic Association, 3, 1134–1155. https://doi.org/10.1162/1542476054729400

30.

Sirin

S. R.

(2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. https://doi.org/10.3102%2F00346543075003417

31.

Takeuchi

L. M.

(2011). Families matter: Designing media for a digital age. New York: The JoanGanz Cooney Center at Sesame Workshop. Retrieved from http://joanganzcooneycenter.org/Reports-29.html20

32.

Timss

Pirls International Study

(2018). TIMSS study: Context Questionnaires. Retrieved from https://timssandpirls.bc.edu/timss2015/questionnaires/index.html

33.

Van de Werfhorst

H. G.

Mijs

J. J. B.

(2010). Achievement inequality and the institutional structure of educational systems: A comparative perspective. Annual Review of Sociology, 36, 407–428. https://doi.org/10.1146/annurev.soc.012809.102538

34.

Yang

(2003). Dimensions of socio-economic status and their relationship to mathematics and science achievement at individual and collective levels. Scandinavian Journal of Educational Research, 47(1). https://doi.org/10.1080/0031383032000033317

35.

Yin

Fishbein

(2020). Creating and interpreting the TIMSS 2019 context questionnaire scales. In Martin

M. O.

von Davier

Mullis

I. V. S.

(Eds.), Methods and procedures: TIMSS 2019 technical report (pp. 16.1–16.331). TIMSS & PIRLS International Study Center, Boston College. https://timssandpirls.bc.edu/timss2019/methods/chapter-16.html