Predictive model for the classification of university students at risk of academic loss

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.