• Shaljan AreepattamannilEmail author
  • Berinderjeet Kaur


This study, employing hierarchical linear modeling (HLM), sought to investigate the student-level and school-level factors associated with the science achievement of immigrant and non-immigrant students among a national sample of 22,646 students from 896 schools in Canada. While student background characteristics such as home language, family wealth, and socioeconomic status were significant predictors of science achievement for non-immigrant students, these factors were not significantly associated with immigrant student science achievement. Student attitudes, engagement, and motivation in science and information and communication technology familiarity were significant predictors of science achievement for both immigrant and non-immigrant students. Whereas teacher shortage was associated with science achievement for immigrant students, school size was associated with science achievement for non-immigrant students. Implications of the findings are discussed.

Key words

hierarchical linear modeling immigrant students non-immigrant students PISA science achievement 


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Copyright information

© National Science Council, Taiwan 2012

Authors and Affiliations

  1. 1.National Institute of EducationNanyang Technological UniversitySingaporeSingapore

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