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

Citation

Sithole, STM and Ran, G and De Lange, P and Tharapos, M and O'Connell, B and Beatson, N, Data mining: will first-year results predict the likelihood of completing subsequent units in accounting programs?, Accounting Education pp. 1-27. ISSN 1468-4489 (2022) [Refereed Article]


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DOI: doi:10.1080/09639284.2022.2075707

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.

Item Details

Item Type:Refereed Article
Keywords:academic performance, accounting students, classification algorithm, educational data mining, performance prediction
Research Division:Commerce, Management, Tourism and Services
Research Group:Accounting, auditing and accountability
Research Field:Accounting, auditing and accountability not elsewhere classified
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in commerce, management, tourism and services
UTAS Author:Sithole, STM (Dr Seedwell Sithole)
UTAS Author:Ran, G (Mr Guang Ran)
UTAS Author:De Lange, P (Professor Paul De Lange)
ID Code:149996
Year Published:2022
Deposited By:College Office - CoBE
Deposited On:2022-05-08
Last Modified:2022-05-16
Downloads:0

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