File(s) under permanent embargo
Data mining: will first-year results predict the likelihood of completing subsequent units in accounting programs?
journal contribution
posted on 2023-05-21, 07:34 authored by Seedwell SitholeSeedwell Sithole, Ran, G, Paul De Lange, Tharapos, M, O'Connell, B, Beatson, NThis 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.
History
Publication title
Accounting EducationPagination
1-27ISSN
1468-4489Department/School
College Office - College of Business and EconomicsPublisher
RoutledgePlace of publication
United KingdomRights statement
© 2022 Informa UK Limited, trading as Taylor & Francis GroupRepository Status
- Restricted