Predictive Model to Identify College Students with High Dropout Rates

Jhoan Keider Hoyos Osorio; Genaro Daza Santacoloma

doi:10.24320/redie.2023.25.e13.5398

Authors

Jhoan Keider Hoyos Osorio Universidad Tecnológica de Pereira https://orcid.org/0000-0002-7169-7963
Genaro Daza Santacoloma Universidad Tecnológica de Pereira https://orcid.org/0000-0002-1429-5925

DOI:

https://doi.org/10.24320/redie.2023.25.e13.5398

Keywords:

dropping out, college students, forecasting, regression analysis

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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