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Predicting first year university progression using early warning signals from accounting education A machine learning approach.pdf (2.47 MB)

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

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posted on 2023-06-10, 06:41 authored by Patricia Everaert, Evelien Opdecam, Hans Van Der HeijdenHans Van Der Heijden
In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of first-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 first-year students from a continental European university show that early warnings from accounting courses are strongly predictive of first-year progression, and more so than data available at the start of the first year. In addition, the further the student is along their journey of the first 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.

History

Publication status

  • Published

File Version

  • Published version

Journal

Accounting Education

ISSN

0963-9284

Publisher

Taylor & Francis

Event name

British Accounting and Finance Association Conference 2022

Event location

Online

Event type

conference

Department affiliated with

  • Accounting and Finance Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2023-04-12

First Open Access (FOA) Date

2023-04-12

First Compliant Deposit (FCD) Date

2023-04-12

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