Indirect Effect of Economic,Social,and Cultural Status on Immigrant Students’ Science Performance Through Science Dispositions: A Multilevel Analysis

Abstract

Determining the ethnic differences in academic performance among immigrant students is important in terms of adapting them into the system they live in. Examining the variables related to students’ performance will be a guide in overcoming the issue. The purpose of this study is to examine the direct effect of economic, social, and cultural status (ESCS) on science performance and the indirect effect of ESCS on the relationship between science dispositions and science literacy in Canadian immigrant students using hierarchical linear modeling. Data were obtained from Program for International Student Assessment (PISA) 2015 science literacy test and student questionnaire and data sets were provided from the official website of PISA. The results of the research show that ESCS had a direct and indirect effect through science dispositions with medium to large on science performance of immigrant students. The impacts of significant variables were discussed and implications for further research were provided.

Keywords

immigrant student science literacy economic social and cultural status science-related disposition multilevel analysis

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References

Acosta

S. T.

Hsu

H.-Y.

(2014). Negotiating diversity: An empirical investigation into family, school and student factors influencing New Zealand adolescents’ science literacy. Educational Studies, 40(1), 98–115. https://doi.org/10.1080/03055698.2013.830243

Adamuti-Trache

Andres

(2008). Embarking on and persisting in scientific fields of study: Cultural capital, gender, and curriculum along the science pipeline. International Journal of Science Education, 30(12), 1557–1584. https://doi.org/10.1080/09500690701324208

Araujo

Saltelli

Schnepf

(2017). Do PISA data justify PISA-based education policy? International Journal of Comparative Education and Development, 19(1), 20–34. https://doi.org/10.1108/IJCED-12-2016-0023

Archambault

Janosz

Dupéré

Brault

M. C.

Andrew

M. M.

(2017). Individual, social, and family factors associated with high school dropout among low-SES youth: Differential effects as a function of immigrant status. The British Journal of Educational Psychology, 87(3), 456–477. https://doi.org/10.1111/bjep.12159

Areepattamannil

Kaur

(2013). Factors predicting science achievement of immigrant and non-immigrant students: A multilevel analysis. International Journal of Science and Mathematics Education, 11(25), 1183–1207. https://doi.org/10.1007/s10763-012-9369-5

Bandura

(1977). Social learning theory. Prentice Hall.

Britner

S. L.

Pajares

(2001). Self-efficacy beliefs, motivation, race, and gender in middle school science. Journal of Women and Minorities in Science and Engineering, 7, 271–285. https://doi.org/10.1615/JWomenMinorScienEng.v7.i4.10

Chai

C. S.

Deng

Wong

B. K. S.

Qian

(2010). South China education majors’ epistemological beliefs and their conceptions of the nature of science. The Asia-Pacific Education Researcher, 19(1), 111–125. https://repository.nie.edu.sg/bitstream/10497/11078/1/TAPER-19-1-111.pdf

Chen

A. J.

Pajares

(2010). Implicit theories of ability of grade 6 science students: Relation to epistemological beliefs and academic motivation and achievement in science. Contemporary Educational Psychology, 35, 75–87. https://doi.org/10.1016/j.cedpsych.2009.10.003

10.

Crowley

Callanan

M. A.

Jipson

Galco

Topping

Shrager

(2001). Shared scientific thinking in everyday parent-child activity. Science Education, 85, 712–732. https://doi.org/10.1002/sce.1035

11.

Gordon

M. M.

(1964). Assimilation in american life: The role of race, religion, and national origins. Oxford University Press on Demand.

12.

Hattie

(2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Taylor & Francis.

13.

Holliday

W. G.

Holliday

B. W.

(2003). Why using international comparative math and science achievement data from TIMSS is not helpful. The Educational Forum, 67(Spring), 250–257. https://doi.org/10.1080/00131720309335038

14.

Kroneberg

(2008). Ethnic communities and school performance among the new second generation in the United States: Testing the theory of segmented assimilation. The ANNALS of the American Academy of Political and Social Science, 620, 138–160. https://doi.org/10.1177/0002716208322714

15.

Krull

J. L.

MacKinnon

D. P.

(2001). Multilevel modeling of individual and group level mediated effects. Multivariate Behavioral Research, 36, 249–277.

16.

Lau

E. S.

Lam

T. Y.

(2015). Effective classroom pedagogy and beyond for promoting scientific literacy: Is there an East Asian model? In Khine

(Ed.), Science education in East Asia (pp. 16–40). Springer.

17.

Lee

A. N.

(2012). Development of a parent’s guide for the Singapore primary science curriculum: Empowering parents as facilitators of their children’s science learning outside the formal classrooms. Asia-Pacific Forum on Science Learning and Teaching, 13(2), 13. https://www.eduhk.hk/apfslt/download/v13_issue2_files/anlee.pdf

18.

Lefton

L. A.

(1991). Psychology (4th ed.). Allyn & Bacon.

19.

Lin

T.-J.

Deng

Chai

C.-S.

Tsai

C.-C.

(2013). High school students’ scientific epistemological beliefs, motivation in learning science, and their relationships: A comparative study within the Chinese culture. International Journal of Educational Development, 33, 37–47. https://doi.org/10.1016/j.ijedudev.2012.01.007

20.

Liu

Hsieh

P.-H.

Cho

Schallert

D. L.

(2006). Middle school students’ self-efficacy, attitudes, and achievement in a computer-enhanced problem-based learning environment. Journal of Interactive Learning Research, 17(3), 225–242. http://jabba.edb.utexas.edu/minliu/liuHsieh06.pdf

21.

Lyons

(2006). Different countries, same science classes: Students’ experiences of school science in their own words. International Journal of Science Education, 28(6), 591–613. https://doi.org/10.1080/09500690500339621

22.

Martin

M. O.

Mullis

I. V. S.

Foy

Stanco

G. M.

(2012). TIMSS 2011 international results in science. TIMSS & PIRLS International Study Center, Boston College. http://timssandpirls.bc.edu/isc/publications.html

23.

Meredith

J. E.

Fortner

R. W.

Mullins

G. W.

(1997). Model of affective learning for nonformal science education facilities. Journal of Research in Science Teaching, 34(8), 805–818. https://doi.org/10.1002/(SICI)1098-2736(199710)34:8<805::AID-TEA4>3.0.CO;2-Z

24.

Migrant Integration Policy Index. (2015). Education: Key findings. http://www.mipex.eu/education

25.

Murat

Frederic

(2015). Institutions, culture and background: The school performance of immigrant students. Education Economics, 23(5), 612–630. https://doi.org/10.1080/09645292.2014.894497

26.

National Household Survey. (2011). https://www12.statcan.gc.ca/nhs-enm/2011/as-sa/99-010-x/99-010-x2011001-eng.cfm

27.

Niemann

Martens

Teltemann

(2017). PISA and its consequences: Shaping education policies through international comparisons. European Journal of Education Research, Development and Policy, 52(2), 177–183. https://doi.org/10.1111/ejed.12220

28.

Organisation for Economic Co-operation and Development. (2005). School sampling preparation manual. OECD Publishing. http://www.oecd.org/dataoecd/55/55/39829698.pdf

29.

Organisation for Economic Co-operation and Development. (2006). Where immigrant students succeed: A comparative review of performance and engagement in PISA 2003. OECD Publishing.

30.

Organisation for Economic Co-operation and Development. (2007). PISA 2006: Science competencies for tomorrow’s world: Analysis (Vol. 1). OECD Publishing.

31.

Organisation for Economic Co-operation and Development. (2012). Untapped skills: Realising the potential of immigrant students. OECD Publishing.

32.

Organisation for Economic Co-operation and Development. (2013). What can immigrant students tell us about the quality of education systems? (PISA in Focus, No 33). OECD Publishing.

33.

Organisation for Economic Co-operation and Development. (2015). Helping immigrant students to succeed at school—And beyond. OECD Publishing. https://www.oecd.org/education/Helping-immigrant-students-to-succeed-at-school-and-beyond.pdf

34.

Organisation for Economic Co-operation and Development. (2016). PISA 2015 results (volume I): Excellence and equity in education. OECD Publishing. https://doi.org/10.1787/9789264266490-en

35.

Organisation for Economic Co-operation and Development. (2017). PISA 2015 technical report. OECD Publishing. https://www.oecd.org/pisa/sitedocument/PISA-2015-technical-report-final.pdf

36.

Portes

Patricia

F.-K.

Haller

(2005). Segmented assimilation on the ground: The new second generation in early adulthood. Ethnic and Racial Studies, 28(6), 1000–1040. https://doi.org/10.1080/01419870500224117

37.

Portes

Zhou

(1993). The new second generation: Segmented assimilation and its variants. The ANNALS of the American Academy of Political and Social Science, 530, 74–96. https://doi.org/10.1177/0002716293530001006

38.

Raudenbush

S. W.

Bryk

A. S.

(2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). SAGE.

39.

Raudenbush

S. W.

Bryk

A. S.

Cheong

Y. F.

Congdon

(2004). HLM6: Hierarchical linear and nonlinear modeling. Scientific Software International.

40.

Rumbaut

R. G.

(1997). Assimilation and its discontents: Between rhetoric and reality. International Migration Review, 31(4), 922–959. https://doi.org/10.1177/019791839703100406

41.

Rumberger

R. W.

(2011). Dropping out: Why students drop out of high school and what can be done about it. Harvard University Press.

42.

Schoon

Parsons

(2002). Teenage aspirations for future careers and occupational outcomes. Journal of Vocational Behaviour, 60(2), 262–288. https://doi.org/10.1006/jvbe.2001.1867

43.

Sellar

Lingard

(2014). The OECD and the expansion of PISA: New global modes of governance in education. British Educational Research Journal, 40(6), 917–936. https://doi.org/10.1002/berj.3120

44.

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/10.3102/00346543075003417

45.

Snijders

Bosker

(2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). SAGE.

46.

Sobel

M. E.

(1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312. https://doi.org/10.2307/270723

47.

Tuan

H. L.

Chin

C. C.

Shieh

S. H.

(2005). The development of a questionnaire to measure students’ motivation towards science learning. International Journal of Science Education, 27(6), 639–654. https://doi.org/10.1080/0950069042000323737

48.

Volante

Klinger

Bigili

Seigel

(2017). Making sense of the performance (dis)advantage for immigrant students across Canada. Canadian Journal of Education, 40(3), 329–361. https://files.eric.ed.gov/fulltext/EJ1157248.pdf

49.

White

K. R.

(1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91(3), 461–481. https://doi.org/10.1037/0033-2909.91.3.461

50.

Willms

J. D.

(2003). Ten hypotheses about socioeconomic gradients and community differences in children’s developmental outcomes. Human Resources Development Canada. https://files.eric.ed.gov/fulltext/ED505297.pdf

51.

(2005). The role of plausible values in large-scale surveys. Studies in Educational Evaluation, 31, 114–128. https://doi.org/10.1016/j.stueduc.2005.05.005

52.

Zhang

Zyphur

M. J.

Preacher

K. J.

(2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12(4), 695–719. https://doi.org/10.1177/109442810832745

53.

Zimmerman

B. J.

(2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82–91. https://doi.org/10.1006/ceps.1999.1016