Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 2.65 MB
Add to list
  1. Search results
  2. Predicting first-year university prog...

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

Patricia Everaert (UGent) , Evelien Opdecam (UGent) and Hans van der Heijden
(2024) ACCOUNTING EDUCATION. 33(1). p.1-26
Author
Organization
Abstract
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.
Keywords
ACADEMIC-PERFORMANCE, STUDENT DEPARTURE, DEGREE COMPLETION, DROP-OUT, DETERMINANTS, ATTRITION, GENDER, LEVEL, MODEL, Accounting education, machine learning, university progression, random, forest

Downloads

  • Download
    Predicting first-year university progression using early warning signals from accounting education.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 2.65 MB

Citation

Please use this url to cite or link to this publication:

MLA
Everaert, Patricia, et al. “Predicting First-Year University Progression Using Early Warning Signals from Accounting Education : A Machine Learning Approach.” ACCOUNTING EDUCATION, vol. 33, no. 1, 2024, pp. 1–26, doi:10.1080/09639284.2022.2145570.
APA
Everaert, P., Opdecam, E., & van der Heijden, H. (2024). Predicting first-year university progression using early warning signals from accounting education : a machine learning approach. ACCOUNTING EDUCATION, 33(1), 1–26. https://doi.org/10.1080/09639284.2022.2145570
Chicago author-date
Everaert, Patricia, Evelien Opdecam, and Hans van der Heijden. 2024. “Predicting First-Year University Progression Using Early Warning Signals from Accounting Education : A Machine Learning Approach.” ACCOUNTING EDUCATION 33 (1): 1–26. https://doi.org/10.1080/09639284.2022.2145570.
Chicago author-date (all authors)
Everaert, Patricia, Evelien Opdecam, and Hans van der Heijden. 2024. “Predicting First-Year University Progression Using Early Warning Signals from Accounting Education : A Machine Learning Approach.” ACCOUNTING EDUCATION 33 (1): 1–26. doi:10.1080/09639284.2022.2145570.
Vancouver
1.
Everaert P, Opdecam E, van der Heijden H. Predicting first-year university progression using early warning signals from accounting education : a machine learning approach. ACCOUNTING EDUCATION. 2024;33(1):1–26.
IEEE
[1]
P. Everaert, E. Opdecam, and H. van der Heijden, “Predicting first-year university progression using early warning signals from accounting education : a machine learning approach,” ACCOUNTING EDUCATION, vol. 33, no. 1, pp. 1–26, 2024.
@article{01GWKW1AJ8FRPJSACP4S5B2FG3,
  abstract     = {{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.}},
  author       = {{Everaert, Patricia and Opdecam, Evelien and  van der Heijden, Hans}},
  issn         = {{0963-9284}},
  journal      = {{ACCOUNTING EDUCATION}},
  keywords     = {{ACADEMIC-PERFORMANCE,STUDENT DEPARTURE,DEGREE COMPLETION,DROP-OUT,DETERMINANTS,ATTRITION,GENDER,LEVEL,MODEL,Accounting education,machine learning,university progression,random,forest}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{1--26}},
  title        = {{Predicting first-year university progression using early warning signals from accounting education : a machine learning approach}},
  url          = {{http://doi.org/10.1080/09639284.2022.2145570}},
  volume       = {{33}},
  year         = {{2024}},
}
Altmetric
View in Altmetric