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Modelling Students at Risk

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Author Info

  • 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. Copyright Blackwell Publishing Ltd/University of Adelaide and Flinders University of South Australia 2004.

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Australian Economic Papers.

Volume (Year): 43 (2004)
Issue (Month): 2 (06)
Pages: 158-173

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Handle: RePEc:bla:ausecp:v:43:y:2004:i:2:p:158-173

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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0004-900X

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Cited by:
  1. Prowse, Victoria, 2012. "Modeling employment dynamics with state dependence and unobserved heterogeneity," MPRA Paper 38038, University Library of Munich, Germany, revised 10 Apr 2012.
  2. 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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