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Cited by 10Online Learning and AnalyticsEvasion and Academic Success FactorsCustomer churn and segmentation

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Predictive Model to Identify College Students with High Dropout Rates

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Hoyos Osorio, Jhoan Keider, Daza Santacoloma, Genaro·2023·Revista Electrónica de Investigación Educativa
DOI 10.24320/redie.2023.25.e13.5398
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Abstract

Decreasing student attrition rates is one of the main objectives of most higher education institutions. However, to achieve this goal, universities need to accurately identify and focus their efforts on students most likely to quit their studies before they graduate. This has given rise to a need to implement forecasting models to predict which students will eventually drop out. In this paper, we present an early warning system to automatically identify first-semester students at high risk of dropping out. The system is based on a machine learning model trained from historical data on first-semester students. The results show that the system can predict “at-risk” students with a sensitivity of 61.97%, which allows early intervention for those students, thereby reducing the student attrition rate.

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Metadata

Authors
Hoyos Osorio, J. K., & Daza Santacoloma, G.
ORCID
Venue
Revista Electrónica de Investigación Educativa
Publisher
Universidad Autónoma de Baja California
Published
2023-05-03
Volume
25
Pages
1-10
ISSN
1607-4041 (ISSN-L)
DOI
10.24320/redie.2023.25.e13.5398
OpenAlex
W4367856884
Cited by
10
References
0
Citations by year
2026: 2 · 2025: 3 · 2024: 3 · 2023: 2
FWCI
2.28
Type
journal article
Language
en
Open access
Licence
Locations
  • Revista Electrónica de Investigación Educativa (publishedVersion, cc-by-nc)
  • Dialnet (Universidad de la Rioja) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Education › Evasion and Academic Success Factors
  • Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
Keywords
student dropout prediction, student attrition, early warning systems, at-risk students, first-semester students, university students, early intervention, predictive modeling

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