Predicting first-year university progression using early warning signals from accounting education: A machine learning approach

Abstract

In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of fi rst-year progression outcomes, and whether this data is more predictive than personal data (such as gender and prior achievement). Using a machine learning approach, results from a sample of 609 fi rst-year students from a continental European university show that early warnings from accounting courses are strongly predictive of fi rst-year progression, and more so than data available at the start of the fi rst year. In addition, the further the student is along their journey of the fi rst undergraduate year, the more predictive the accounting engagement and performance data becomes for the prediction of programme progression outcomes. Our study contributes to the study of early warning signals for dropout through machine learning in accounting education, suggests implications for accounting educators, and provides useful pointers for further research in this area.