Advertisement

EQUITY IN MATHEMATICS AND SCIENCE OUTCOMES: CHARACTERISTICS ASSOCIATED WITH HIGH AND LOW ACHIEVEMENT ON PISA 2006 IN IRELAND

  • Lorraine GilleeceEmail author
  • Jude Cosgrove
  • Nick Sofroniou
Article

Abstract

Equity in education is a key concern internationally; however, it is rare that this issue is examined separately for low- and high-achieving students and concurrently across different subject domains. This study examines student and school background characteristics associated with low and high achievement in mathematics and science on the Programme for International Student Assessment. Based on the results of a multilevel multinomial model of achievement for each domain, findings indicate that a greater number of the variables examined are associated with low rather than high achievement. At student level, home language, intention to leave school early, socioeconomic status, grade level, cultural capital, and books in the home are significantly associated with achievement in mathematics and science. At school level, only school average socioeconomic status is statistically significant in the models. Significant gender differences are found in the distribution of high and low achievers, which vary across the domains. In mathematics, females are more likely to be low achievers while males are more likely to be high achievers. In science, gender interacts with early school-leaving intent whereas males intending to leave school early are more likely to be in the low-achieving group than females intending to leave early. Conclusions emphasise the need for targeting resources aimed at promoting equity in outcomes at student level as well as at school level. Future work may extend the current analyses by incorporating domain-specific variables or examining cross-country differences.

Key words

achievement equity Ireland mathematics PISA science 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aitkin, M., Francis, B., & Hinde, J. (2005). Statistical modelling in GLIM 4. London: Oxford University Press.Google Scholar
  2. Bourdieu, P. (1977). Cultural reproduction and social reproduction. In J. Karabel & A. Halsey (Eds.), Power and ideology in education (pp. 487–511). London: Oxford University Press.Google Scholar
  3. Bourdieu, P., & Passeron, J. (1977). Reproduction in education, society and culture. Sheffield: Sheffield Region Centre for Science and Technology.Google Scholar
  4. Chiu, M. M., & Xihua, Z. (2008). Family and motivation effects on mathematics achievement: Analyses of students in 41 countries. Learning and Instruction, 18(4), 321–336.CrossRefGoogle Scholar
  5. Commission of the European Communities (2008). Migration and mobility: Challenges and opportunities for EU education systems (Green Paper). Brussels, Belgium: Author.Google Scholar
  6. Conaty, C. (2002). Including all: Home, school and community united in education. Dublin: Veritas.Google Scholar
  7. Cosgrove, J., & Cunningham, R. (in press). A multilevel model of science achievement of Irish students participating in the 2006 Programme for International Student Assessment. Irish Journal of Education. Google Scholar
  8. Cosgrove, J., & Gilleece, L. (2009, September). A profile of high and low achievers in Ireland: Reading literacy in PISA 2000, 2003 and 2006. Paper presented at the PISA research conference, Kiel, Germany.Google Scholar
  9. Cosgrove, J., Shiel, G., Sofroniou, N., Zastrutzki, S., & Shortt, F. (2005). Education for life: The achievements of 15-year-olds in Ireland in the second cycle of PISA. Dublin: Educational Research Centre.Google Scholar
  10. Department of Education & Science (2005). DEIS (Delivering Equality of Opportunity In School): An action plan for educational inclusion. Dublin: Author.Google Scholar
  11. Eivers, E., Shiel, G., & Cunningham, R. (2008). Ready for tomorrow’s world? The competencies of Ireland’s 15-year-olds in PISA 2006. Dublin: Educational Research Centre.Google Scholar
  12. Goldstein, H. (1995). Interpreting international comparisons of student achievement. Paris: UNESCO.Google Scholar
  13. Government of Ireland (1998). Education Act (No. 51 of 1998). Dublin: The Stationery Office.Google Scholar
  14. Halpern, D. F., Benbow, C. P., Geary, D. C., Gur, R. C., Shibley Hyde, J., & Gernsbacher, M. A. (2007). The science of sex differences in science and mathematics. Psychological Science in the Public Interest, 8(1), 1–51.Google Scholar
  15. Hampden-Thompson, G., & Johnston, J. S. (2006). Variation in the relationship between nonschool factors and student achievement. Washington, DC: National Center for Education Statistics.Google Scholar
  16. Kellaghan, T. (2001). Towards a definition of educational disadvantage. Irish Journal of Education, 32, 3–22.Google Scholar
  17. O’Brien, S., & Ó Fathaigh, M. (2004, April). Bringing in Bourdieu’s theory of social capital: Renewing learning partnership approaches to social inclusion. Paper presented at the Annual Conference of the Educational Studies Association of Ireland, NUI Maynooth, Ireland.Google Scholar
  18. Organisation for Economic Co-operation and Development (1999). Classifying educational programmes: Manual for ISCED-97 implementation in OECD countries. Paris: Author.Google Scholar
  19. Organisation for Economic Co-operation and Development (2001). Knowledge and skills for life: First results from PISA 2000. Paris: Author.Google Scholar
  20. Organisation for Economic Co-operation and Development (2004). Learning for tomorrow’s world—First results from PISA 2003. Paris: Author.Google Scholar
  21. Organisation for Economic Co-operation and Development (2005). PISA 2003 data analysis manual. Paris: Author.Google Scholar
  22. Organisation for Economic Co-operation and Development (2007). PISA 2006 science competencies for tomorrow’s world (vol. 1: Analysis). Paris: Author.Google Scholar
  23. Organisation for Economic Co-operation and Development (2009). PISA 2006 technical report. Paris: Author.Google Scholar
  24. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis. Newbury Park, CA: Sage.Google Scholar
  25. Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., & Congdon, R. T. (2004). HLM 6: Hierarchical linear and non-linear modelling. Lincolnwood, IL: Scientific Software International.Google Scholar
  26. Raudenbush, S. W., & Willms, J. D. (1995). The estimation of school effects. Journal of Educational and Behavioral Statistics, 20, 307–335.Google Scholar
  27. Shiel, G., Cosgrove, J., Sofroniou, N., & Kelly, A. (2001). Ready for life: The literacy achievements of Irish 15-year olds. Dublin: Educational Research Centre.Google Scholar
  28. Van de Gaer, E., Gebhardt, E., & Schulz, W. (2009, September). The relationship between achievement and self-concept: A cross-country investigation. Paper presented at the PISA research conference, Kiel, Germany.Google Scholar
  29. Williams, T., & Williams, K. (in press). Self-efficacy and performance in mathematics: Reciprocal determinism in 33 nations. Journal of Educational Psychology. Google Scholar
  30. Willms, J. D. (2002). Ten hypotheses about socioeconomic gradients and community differences in children’s developmental outcomes. Montreal, Quebec: Statistics Canada.Google Scholar

Copyright information

© National Science Council, Taiwan 2010

Authors and Affiliations

  • Lorraine Gilleece
    • 1
    Email author
  • Jude Cosgrove
    • 1
  • Nick Sofroniou
    • 2
  1. 1.Educational Research CentreDublin 9Ireland
  2. 2.WJECCardiffUK

Personalised recommendations