Predicting Student Attrition with Data Mining Methods
Abstract
Affecting university rankings, school reputation, and financial well-being, student retention has become one of the most important measures of success for higher education institutions. From the institutional perspective, improving student retention starts with a thorough understanding of the causes behind the attrition. Such an understanding is the basis for accurately predicting at-risk students and appropriately intervening to retain them. In this study, using 8 years of institutional data along with three popular data mining techniques, we developed analytical models to predict freshmen student attrition. Of the three model types (artificial neural networks, decision trees, and logistic regression), artificial neural networks performed the best, with an 81% overall prediction accuracy on the holdout sample. The variable importance analysis of the models revealed that the educational and financial variables are the most important among the predictors used in this study.
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Delen (2011) found that the ANN model worked best with an accuracy rate of 81.19% and concluded that the most important predictors of student drop-out are related to the student's past and present academic achievement, and whether they receive financial support.
“Based on a 10-fold cross validation, Delen (2011) found that the ANN model worked best with an accuracy rate of 81.19% (see Table 7) and he concluded that the most important predictors of student drop-out are related to the student's past and present academic achievement, and whether they receive financial support.”
From Systematic review of research on artificial intelligence applications in higher education – where are the educators? (2019) · cites this paper as Delen (2011)
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Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
“Based on a 10-fold cross validation, Delen (2011) found that the ANN model worked best with an accuracy rate of 81.19% (see Table 7) and he concluded that the most important predictors of student drop-out are related to the student’s past and present academic achievement, and whether they receive…”
MethodInfluentialAguiar, E., Chawla, N. V., Brockman, J., Ambrose, G. A., & Goodrich, V. (2014) · Proceedings of the Fourth International Conference on Learning Analytics And Knowledge · doi:10.1145/2567574.2567583 · Cited by 69 · see citing works
- Authors
- Delen, D.
- Affiliations
- Oklahoma State University (US)
- Corresponding author
- Delen, Dursun
- ORCID
- Delen, Dursun
- Venue
- Journal of College Student Retention: Research, Theory & Practice
- Publisher
- SAGE Publications
- Published
- 2011-05
- Volume
- 13
- Issue
- 1
- Pages
- 17-35
- ISSN
- 1521-0251 (ISSN-L)
- 1541-4167
- OpenAlex
- W2045487331
- Cited by
- 124
- References
- 0
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 13 · 2023: 13 · 2022: 16 · 2021: 13 · 2020: 14 · 2019: 10 · 2018: 3 · 2017: 7 · 2016: 3 · 2015: 7 · 2014: 5 · 2013: 4 · 2012: 1
- FWCI
- 5.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of College Student Retention Research Theory & Practice (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Demography › Evasion and Academic Success Factors
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Keywords
- student attrition, student retention, first-year students, at-risk students, attrition prediction, artificial neural networks, decision tree, logistic regression, feature importance, financial factors, educational factors, higher education institutions
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