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Modelling Students at Risk

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  • Diane M. Dancer
  • Denzil G. Fiebig

Abstract

Using a sample of several hundred students we model progression in a first‐year econometrics course. Our primary interest is in determining the usefulness of these models in the identification of ‘students at risk’. This interest highlights the need to distinguish between students who drop the course and those who complete but who ultimately fail. Such models allow identification and quantification of the factors that are most important in determining student progression and thus make them a potentially useful aid in educational decision making. Our main findings are that Tertiary Entrance Rank (TER), mathematical aptitude, being female and attendance in tutorials are all good predictors of success but amongst these factors only attendance is significant in discriminating between students who fail and those who discontinue. Also, there are differences across degree programs and, in particular, students in Combined Law are very likely to pass but, if they are at risk, they are much more likely to discontinue than to fail.

Suggested Citation

  • Diane M. Dancer & Denzil G. Fiebig, 2004. "Modelling Students at Risk," Australian Economic Papers, Wiley Blackwell, vol. 43(2), pages 158-173, June.
    • Handle: RePEc:bla:ausecp:v:43:y:2004:i:2:p:158-173
    DOI: 10.1111/j.1467-8454.2004.00222.x
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    Cited by:

    1. Victoria Prowse, 2012. "Modeling Employment Dynamics With State Dependence and Unobserved Heterogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 411-431, April.
    2. Girijasankar Mallik & John Lodewijks, 2010. "Student Performance in a Large First Year Economics Subject: Which Variables are Significant?," Economic Papers, The Economic Society of Australia, vol. 29(1), pages 80-86, March.
    3. Nolan, Elizabeth & Ahmadi-Esfahani, Fredoun Z., 2007. "Predicting performance in undergraduate agricultural economics," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(1), pages 1-15.
    4. Mallik, Girijasankar & Shankar, Sriram, 2016. "Does prior knowledge of economics and higher level mathematics improve student learning in principles of economics?," Economic Analysis and Policy, Elsevier, vol. 49(C), pages 66-73.
    5. Hong il Yoo, 2012. "A new condition for pooling states in multinomial logit," Discussion Papers 2012-48, School of Economics, The University of New South Wales.

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