Margaret’s undergraduate training was in Mathematics and Statistics. She has completed a Ph.D on the topic of the application of item response theory to problem solving.

Since 1992, she has worked as a psychometrician at the Australian Council for Educational Research (ACER) and at the University of Melbourne. Margaret carried out data analyses for a number of assessment projects, provided advice to both test developers and data analysts, and prepared proposals for project tenders in the area of educational assessment.

Margaret is a co-author of the software program ConQuest for the analysis of item response data. This program has the flexibility of fitting a large number of different item response models, and is now commercially available and widely used both within Australia and internationally.

Margaret had a close involvement in large-scale international studies, taking a leading role in scaling the data in both the Third International Mathematics and Science Study (TIMSS) and the Program for International Student Assessment (PISA). Margaret was the deputy project director of PISA between 1999 and 2002. In this role, Margaret managed the test development as well as the data analysis. Margaret also made substantial contributions to PISA mathematics and problem solving item writing. She has been actively involved in the PISA technical advisory group, as well as the PISA problem-solving expert group.

Margaret has conducted numerous workshops on item response modelling in Australia and internationally.

• Member of the psychometric society
• Member of the Statistical Society of Australia
• Member of MERGA (The Mathematics Education Research Group Of Australasia)
• Registered teacher of Victorian Institute of Teaching

B.Sc.(Hons.), Grad. Dip. (Computer Science), M.Ed., Dip.Ed., Ph.D

Test and Scale Development
Quantitative Methods
Large-scale Survey Research

Margaret’s main area of research is in the development of item response modelling and its applications, particularly in the context of large-scale assessments such as international and national assessments. Margaret also has a keen interest in computer delivered assessments and the use of technology in educational assessment.

Areas of Supervision:

Mathematical modelling of item responses in educational assessments.

Wu, M,L., & Les, M. (2008). The difference between boys and girls. EQ. Summer 2008, p35-36. Curriculum Corporation. Melbourne.

Wu, M.L. (2008). A Comparison of PISA and TIMSS 2003 achievement results in Mathematics and Science. Paper presented at the Third IEA Research Conference, Taipei, September, 2008.

Wu, M. L., & Les, M. (2008). An analysis of the differential performance of girls and boys on an English test. Paper presented at the PROMS (Pacific Rim Objective Measurement Symposium) 2008 in Tokyo.

Wu, M. L. (2008). Improving the measurement of Reading scores by using mathematics and science scores. Keynote address presented at the PROMS (Pacific Rim Objective Measurement Symposium) 2008 in Tokyo.

Wu, M. L. (2008). A holistic assessment environment catering for individual students. Invited paper presented at the International Education Colloquium on Holistic Assessment, May 4-7, 2008, Kuching, Malaysia.

Adams, R. J., & Wu, M. L. (2008). The Construction and Implementation of User-Defined Fit Tests for Use with Marginal Maximum Likelihood Estimation and Generalised Item Response Models. In Duckor. B, & Brown, N. (Eds.), Advances in Rasch Measurement. Volume 2. In press.

Wu, M.L. (2008). A Comparison of PISA and TIMSS 2003 achievement results in Mathematics. Paper presented at the AERA Annual Meeting, New York, March, 2008.

Wu, M. L., Adams, R. J., Wilson, M. R., Haldane, S.A. (2007). ACERConQuest Version 2: Generalised item response modelling software. Camberwell: Australian Council for Educational Research.

Wu, M.L. (2007). Measurement Errors in Perspective. Invited paper presented at the PROMS (Pacific Rim Objective Measurement Symposium) 2007 in Taiwan.

Adams, R.J., & Wu, M.L. (2007). The mixed-coefficient multinomial logit model: A generalized form of the Rasch model. In M. von Davier & C.H. Carstensen (Eds.) Multivariate and mixture distribution Rasch models: Extensions and applications, (pp. 57-76). Springer Verlag.

Adams, R.J., Wu, M.L., & Carstensen, C.H. (2007). Application of multivariate Rasch models in international large scale educational assessment. In M. von Davier & C.H. Carstensen (Eds.) Multivariate and mixture distribution Rasch models: Extensions and applications, (pp. 271-280). Springer Verlag.

Wu, M.L., & Zhang, D. (2006). An overview of the mathematics curricula in the west and east discussions on the findings of the Chongquing paper. In F. Leung., K.D. Graf., & F. Lopaz-Rea (Eds.), Mathematics Education in Different Cultural Traditions - A comprehensive study of east Asia and the West, pp. 181-193. USA: Springer.

Wu, M., & Park, K., et el., (2006). Curriculum. In F. leung., K.D. Graf., & F. Lopez-Rea (Eds.), Mathematics Education in Different Cultural Traditions - A comprehensive study of east Asia and the west, pp 153-157. USA: Springer.

Wu, M.L. (2006). A comparison of mathematics performance between east and west: What OISA and TIMSS an tell us. In F. Leung., K.D. Graf., & F. Lopez-Rea (Eds.), Mathematics Education in Different cultural Traditions - A Comparative study of east Asia and the west, pp 239-259. USA: Springer.

Nguyen, T.K.C., Wu, M.L., & Gillis, S. (in press). Factors influencing pupil achievement in SACMEQ II- Botswana: An Application of Structural Equation Modelling. International Journal of Education Research.

Wu, M.L. & Adams, R.J. (2006, to appear). Modelling Mathematics Problem Solving Item Responses Using a Multidimensional IRT model. Mathematics Education Research Journal.

Wu, M.L. (2005). The role of plausible values in large-scale surveys. Studies in Educational Evaluation, 31(2005, 114-128.

Wu, M.L. (2005). The impact of PISA on Mathematics Education: Linking Mathematics and the Real World. Educational Journal, 31/32(2/1), 121.

Adams, R. J., Wilson, M. R., & Wu, M. L. (1997). Multilevel item response models: An approach to errors in variables regre

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