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Data mining: will first-year results predict the likelihood of completing subsequent units in accounting programs?

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  • Seedwell T. M. Sithole
  • Guang Ran
  • Paul de Lange
  • Meredith Tharapos
  • Brendan O’Connell
  • Nicola Beatson

Abstract

This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students’ current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to examine 640 accounting students enrolled in an undergraduate accounting program at an Australian university. A significant contribution of this study is improved prediction of academic performance and identification of characteristics of students deemed to be at risk. By partitioning students into sub-groups based on tertiary entrance scores and employing clustering of study units, this study facilitates a more nuanced understanding of predictor attributes. Key findings were the dominance of a cluster of second year units in predicting students’ later academic performance; that gender did not influence performance; and that performance in first year at university, rather than secondary school grades, was the most important predictor of subsequent academic performance.

Suggested Citation

  • Seedwell T. M. Sithole & Guang Ran & Paul de Lange & Meredith Tharapos & Brendan O’Connell & Nicola Beatson, 2023. "Data mining: will first-year results predict the likelihood of completing subsequent units in accounting programs?," Accounting Education, Taylor & Francis Journals, vol. 32(4), pages 409-444, July.
    • Handle: RePEc:taf:accted:v:32:y:2023:i:4:p:409-444
    DOI: 10.1080/09639284.2022.2075707
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