Examining Factors Affecting Science Achievement of Hong Kong in PISA 2006 Using Hierarchical Linear Modeling
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AbstractThis study uses hierarchical linear modeling to examine the influence of a range of factors on the science performances of Hong Kong students in PISA 2006. Hong Kong has been consistently ranked highly in international science assessments, such as Programme for International Student Assessment and Trends in International Mathematics and Science Study; therefore, an exploration of the factors that affect science performances of Hong Kong students can give a lens to examine how science education can be improved in Hong Kong and other countries. The analyses reveal that student backgrounds as male, at higher grade levels, and born in mainland (when in the same grade) are associated with better science performance. Among the attitudinal factors, enjoyment of science and self-efficacy in science play important roles in scientific achievements. Most of the parental factors, on the other hand, are not having significant impacts on achievement after student attitudes are taken into account, with only parents' value of science having a small effect. School student intake is found to be a strong predictor of school average achievement, as well as a major mediator of the effects of school enrollment size and school socio-economic status. The findings differ from recently reported results, which suggested that school enrollment size was associated with achievement. This study also points out the problems of the use of science instruction time as a school-level variable to explain science achievement in Hong Kong. © 2014 © 2014 Taylor & Francis.
All Author(s) ListLam T.Y.P., Lau K.C.
Journal nameInternational Journal of Science Education
Detailed descriptionInternational Journal of Science Education.\n\nTo ORKTS: One coauthor. Lam Yuk Ping, is the staff of Hong Kong Centre for International Student Assess
Volume Number36
Issue Number15
PublisherTaylor & Francis
Place of PublicationUnited Kingdom
Pages2463 - 2480
LanguagesEnglish-United Kingdom
KeywordsAttitudes toward science, Hierarchical linear modeling, PISA, Scientific literacy

Last updated on 2020-23-05 at 23:55