Predictive Model for the classification of university students at risk of academic loss
Bibliographic Data
| ID | 21437757 |
|---|---|
| Authors | María Cristina Gamboa Mora (0000-0001-8459-099X, Universidad Nacional Abierta y a Distancia), Felix Vivián Mohr (0000-0002-9293-2424, Universidad de La Sabana), Vicky Ahumada De La Rosa (0000-0002-8797-331X, Universidad Nacional Abierta y a Distancia), Sulma Paola Vera-Monroy (0000-0002-7573-4151, Universidad de La Sabana), Alexander Mejía-Camacho (0000-0003-4949-2045, University of Cundinamarca) |
| Year | 2024 |
| Volume | 26 |
| Issue | 47 |
| Publication date | 2024-06-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educación y Humanismo (JOURNAL) |
| Journal identifiers | ISSN: 0124-2121 • E-ISSN: 2665-2420 |
| Publisher | Universidad Simon Bolivar (PUBLISHER • VE) |
| DOI | 10.17081/eduhum.26.47.6379 |
| OpenAlex | W4400002596 |
| Language | EN |
| References cited | 4 |
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
Mathematics education · Advanced Data Processing Techniques · Computer Science · Foreign Language Teaching Methods · Psychology · Technology and Human Factors in Education and Health
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| Citation velocity | historical |
|---|---|
| Highly cited | No |