@misc{10818/61945,
year = {2024},
url = {http://hdl.handle.net/10818/61945},
abstract = {For higher education institutions, predicting the risk of academic loss is a priority issue due to the resources invested by institutions, students and the academic community in general. Objective: the objective of this research was to propose a suitable model that allows predicting students who are at risk of academic loss in a chemistry course. Methodology: the quasi-experimental, predictive, longitudinal research was developed with data from 103 students from four Colombian universities. To build the model, a comparison of five algorithms was implemented. Data was processed with Jupyter-Python. Results: the logistic regression model (LR) was built based on the students’ results on the Saber 11 test (Colombian nation-wide university admission exam), in which the penalty of false positives with different weights from the false negatives improved the performance of the model. Conclusions: it is concluded that LR is substantially better than grasping or a guessing approach, furthermore, it was shown to perform better than a neural network model. © 2024, Simon Bolivar University (Barranquilla). All rights reserved.},
publisher = {Educacion y Humanismo},
title = {Predictive model for the classification of university students at risk of academic loss},
doi = {10.17081/eduhum.26.47.6379},
author = {Gamboa-Mora M and Vivián-Mohr F and Ahumada De La Rosa V and Vera-Monroy S and Mejía-Camacho A.},
}