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Using Predictive Modelling to Identify Students at Risk of Poor University Outcomes

Author

Listed:
  • Pengfei Jia

    (Department of Economics, Faculty of Business and Law, Auckland University of Technology)

  • Tim Maloney

    (Department of Economics, Faculty of Business and Law, Auckland University of Technology)

Abstract

We use predictive modelling to identify students at risk of not completing their first-year courses and not returning to university in the second year. Our aim is two-fold. Firstly, we want to understand the pathways that lead to unsuccessful first-year experiences at university. Secondly, we want to develop simple, low-cost tools that would allow universities to identify and intervene on vulnerable students when they first arrive on campus. This is why we base our analysis on administrative data routinely collected as part of the enrolment process from a New Zealand university. We assess the ‘target effectiveness’ of our model from a number of perspectives. This approach is found to be substantially more predictive than a previously developed risk tool at this university. Students in the top decile of risk scores account for over 29% of first-year course non-completions and more than 23% of second-year student non-retentions at this university

Suggested Citation

  • Pengfei Jia & Tim Maloney, 2014. "Using Predictive Modelling to Identify Students at Risk of Poor University Outcomes," Working Papers 2014-03, Auckland University of Technology, Department of Economics.
    • Handle: RePEc:aut:wpaper:201403
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    References listed on IDEAS

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    Keywords

    Predictive Risk Modelling; University Failure and Dropout Behaviour; and New Zealand;
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