Factors accounting for mathematics achievement of Singaporean eighth-graders

Asia-Pacific Education Researcher

Volumn 21, Issue 3, 2012, Pages 507-518

  Mohammadpour, Ebrahim ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Achievement; Mathematics; Multilevel; Singapore; TIMSS

Indexed keywords

EID: 84869042112     PISSN: 01195646     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (60)

1. Anderson, L. W. (2004). Increasing teacher effectiveness (2nd ed.). Paris, France: UNESCO.
2. Barker, R., & Gump, P. (1964). Big school, small school: High school size and student behavior. Stanford, CA: Stanford University Press.
3. Catsambis, S. (2005). The gender gap in mathematics: Merely a step function? In A. M. Gallagher & J. C. Kaufman (Eds.), Gender differences in mathematics: An integrative psychological approach (pp. 220-245). Cambridge, UK: Cambridge University Press.
4. Chepete, P. (2008). Modeling of the factors affecting mathematical achievement of Form 1 students in Botswana based on the 2003 Trends in International Mathematics and Science Study (Unpublished doctoral dissertation). Indiana University.
5. Coleman, J. S., Compbell, E. Q., Hobson, C. J., Mcpartland, J., Mood, A. M., Weinfeld, F. D., et al. (1966). Equality of educational opportunity. Washington, DC: Department of Health, Education & Welfare Office of Education.
6. Conant, J. B. (1967). The comprehensive school. New York, NY: McGraw-Hill Book Company.
7. Creemers. B. P. M. (1994). The effective classroom. London, UK: Cassell.
8. Darling-Hammond, L. (2000). Teacher quality and student achievement: A review of state policy evidence. Educational Policy Analysis Archives, 8(1), 1-47.
9. Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-national patterns of gender differences in mathematics: A meta-analysis. Psychological Bulletin, 136(1), 103-127.
10. Foy, P., & Olson, J. F. (2009). International Association for the Evaluation of Educational Achievement (IEA) TIMSS 2007 User Guide for the International Database. Chestnut Hill, MA: Boston College.
11. Greenwald, R., Hedges, L. V., & Laine, R. D. (1996). The effect of school resources on student achievement. Review of Educational Research, 66, 361-396.
12. th ed.). Upper Sadle River, NJ:Prentice Hall.
13. Hanushek, E. A. (1996). A more complete picture of school resource policies. Review of Educational Research, 66, 397-409.
14. Hanushek, E. A. (1997). Assessing the effects of school resources on student performance: An update. Educational Evaluation and Policy Analysis, 19(2), 141-164.
15. Hanushek, E. A. (1999). Some findings from an independent investigation of the Tennessee STAR experiment and from other investigations of class size effects. Educational Evaluation and Policy Analysis, 21 (2), 143-163.
16. Hedges, L. V., Laine, R., & Greenwald, R. (1994). An exchange: Part I: Does money matter? A meta-analysis of studies of the effects of differential school inputs on student outcomes. Educational Researcher, 23(3), 5-14.
17. Hill, P. W., & Rowe, K. J. (1996). Multilevel modelling in school effectiveness research. School Effectiveness and School Improvement, 7(1), 1-34.
18. Howie, S. (2004). A national assessment in mathematics within an international comparative assessment. Perspectives in Education, 22(2), 149-162.
19. Howie, S. (2006). Multi-level factors affecting the performance of South African pupils in mathematics. In S. Howie & T. Plomp (Eds.), Contexts of Learning Mathematics and Science (pp. 157-176). London: Routledge.
20. Hox, J. J. (2009). Multilevel analysis techniques and applications (2nd ed.). London: Routledge.
21. Hyde, J. S., & Mertz, J. E. (2009). Gender, culture, and mathematics performance. Proceedings of the National Academy of Sciences of the United States of America, 106(22), 8801-8807.
22. Jencks, C., Smith, M. S., Ackland, H., Bane, M. J., Cohen, D., Grintlis, H., et al. (1972). Inequality. New York, NY: Basic Books.
23. Jurdek, M. (2009). Toward equity in quality in mathematics education. New York, NY: Springer Science+Business Media, LLC.
24. Kunter, M., Tsai, Y.-M., Klusmann, U., Brunner, M., Krauss, S., & Baumert, J. (2008). Students' and mathematics teachers' perceptions of teacher enthusiasm and instruction. Learning and Instruction, 18(5), 468-482. doi: 10.1016/j. learninstruc.2008.06.008
25. Lamb, S., & Fullarton, S. (2002). Classroom and school factors affecting mathematics achievement: A comparative study of Australia and the United States using TIMSS. Australian Journal of Education, 46(2), 154-171.
26. Lee Van Horn, M. (2003). Assessing the unit of measurement for school climate through psychometric and outcome Analyses of the school climate survey. Educational and Psychological Measurement, 63(6), 1002-1019.
27. Leung, F. (2009). Chinese culture, Islamic culture, and mathematics education. In P. Clarkson & N. Presmeg (Eds.), Critical issues in mathematics education (pp. 135-147). New York, NY: Springer Science+Business Media, LLC.
28. Liou, P. Y. (2010). Cross-national comparisons of the association between student motivation for learning mathematics and achievement linked with school contexts: Results from TIMSS 2007. Minneapolis, MN: University of Minnesota.
29. Liu, W. C., Wang, C. K. J., & Parkins, E. J. (2005). A longitudinal study of students' academic self-concept in a streamed setting: The Singapore context. British Journal of Educational Psychology, 75, 567-586.
30. Lubienski, S. T., Lubienski, C., & Crane, C. C. (2008). Achievement differences and school type: The role of school climate, teacher certification, and instruction. American Journal of Education, 115(1), 97-138.
31. Ma, X., & Kishor, N. (1997). Attitude toward self, social factors, and achievement in mathematics: A meta-analytic review. Educational Psychology Review, 9, 89-120.
32. Ma, X., & Klinger, D. A. (2000). Hierarchical linear modelling of student and school effects on academic achievement. Canadian Journal of Education, 25(1), 41-55.
33. Maccoby, E. (1966). The development of sex differences. Stanford, CA: Stanford University Press.
34. Martin, M. O., Mullis, I. V. S., Gregory, K. D., Hoyle, C., & Shen, C. (2000). Effective schools in science and mathematics. Boston, MA: International Association for the Evaluation of Educational Achievement, TIMSS International Study Center Boston College.
35. Metz, K. (2010). Discrimination. In C. S. Clauss-Ehlers (Ed.), Encyclopedia of cross-Cultural school psychology (pp. 379-382). New York, NY: Springer Science+Business Media, LLC.
36. Mullis, I. V. S., Martin, M. O., & Foy, P. (2008). International Association for the Evaluation of Educational Achievement (IEA) TIMSS 2007 International Mathematics Report. Chestnut Hill, MA: Boston College.
37. Mullis, I. V. S., Martin, M. O., Gonzalez, E. J., & Chrostowski, S. J. (2004). International Association for the Evaluation of Educational Achievement (IEA) TIMSS 2003 International Mathematics Report. Chestnut Hill, MA: Boston College.
38. O'Dwyer, L. M. (2000). Extending the application of multilevel modeling to data from the Third International Mathematics and Science Study (TIMSS). Boston, MA: Boston College Massachusetts.
39. Olson, J. F., Martin, M. O., & Mullis, I. V. S. (2008). International Association for the Evaluation of Educational Achievement (IEA) TIMSS 2007 technical report. Chestnut Hill, MA: Boston College.
40. Quek, G., Goh, K. H., Yen, Y. P., Liu, Y. M., Tan, Q. L., Chin, T. Y., & Lau, C.Y. (2008). Singapore. In I. V. S. Mullis, M. O. Martin, J.F. Olson, D. R. Berger, D. Milne, & G.M. Stanco (Eds.), International Association for the Evaluation of Educational Achievement (IEA) TIMSS 2007 Encyclopedia (Vol. 2, pp. 537-550). Chestnut Hill, MA: Boston College.
41. Ramírez, M. J. (2006). Factors related to mathematics achievement in Chile. In S. Howie & T. Plomp (Eds.), Contexts of learning mathematics and Science (pp. 97-111). London and New York: Routledge.
42. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Application and data analysis methods (2nd ed.). Thousand Oaks, CA:Sage.
43. Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., Congdon, R., & Toit, M. (2004). HLM6 hierarchical linear & nonlinear modeling: Application and data analysis methods. Lincolnwood, IL: SSI Scientific Software.
44. Raudenbush, S. W., & Willms, J. D. (1995). The estimation of school effects. Journal of Educational and Behavioral Statistics, 20(4), 307-335.
45. Reyes, L. H. (1984). Affective variables and mathematics education. The Elementary School Journal, 84, 558-581.
46. Rumberger, R. W., & Palardy, G. J. (2004). Multilevel models for school effectiveness research. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences: Thousand Oaks, CA: Sage.
47. Shavelson, R., McDonnell, L., Oakes, J., Carey, N., & Picus, L. (1987). Indicators systems for monitoring mathematics and science education. Sta. Monica, CA: Rand Corporation.
48. Shavelson, R. J., & Bolus, R. (1982). Self concept: The interplay of theory and methods. Journal of Educational Psychology, 74(1), 3-17.
49. Slavin, R. E. (1999). Class size and student achievement: Small effects of small classes. Educational Psychologist, 24(1), 99-110.
50. Slavin, R. E. (2006). Educational psychology: Theory and practice (8th ed.). Boston, MA: Pearson/Allyn & Bacon.
51. Stevens, J. (2002). Applied multivariate statistics for the social sciences (4th ed.). Mahwah, NJ: Erlbaum.
52. Strand, S., & Winston, J. (2008). Educational aspirations in inner city schools. Educational Studies, 34(4), 249-267.
53. Stronge, J. H. (2007). Qualities of effective teachers (2nd ed.). Alexandria, VA: Association for Supervision and Curriculum Development.
54. Tabachnik, B. G., & Fidell, L. S. (2007). Using multivariate statistics. (5th ed.). Upper Saddle River, NJ: Pearson Education.
55. Teo, T. (2011). Basic concepts of hierarchical linear modelling (HLM) for educational technology research. British Journal of Educational Technology, 42(3), E51-E53.
56. Wagner, P., Schober, B., & Spiel, C. (2008). Time students spend working at home for school. Learning and Instruction, 18(4), 309-320. doi: 10.1016/j.learninstruc.2007.03.002
57. Walberg, H. J. (1987). Psychological environment. In M. J. Dunkin (Ed.), The international encyclopedia of teaching and teacher education (pp. 553-558). Oxford, UK: Pergamon Press.
58. Wang, Z. (2008). Academic Motivation, Mathematics Achievement, and the School Context: Building Achievement Models Using TIMSS 2003. Columbia, MO: University of Missouri.
59. Willms, J. D. (1992). Monitoring school performance: A guide for educators. New York, NY: The Falmer Press.
60. Yang, Y. (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), 22-41.