A Multinomial and Predictive Analysis of Factors Associated with University Dropout
Bibliographic Data
| ID | 5922014 |
|---|---|
| Authors | Tatiana Fernández-Martín (0000-0002-7726-0068, Instituto Tecnológico de Costa Rica), Martín Solís-Salazar, Martín Solís (0000-0003-4750-1198, Instituto Tecnológico de Costa Rica), María Teresa Hernández-Jiménez (0000-0002-5672-5743, Instituto Tecnológico de Costa Rica), Tania Elena Moreira-Mora (0000-0002-8955-0804, Instituto Tecnológico de Costa Rica) |
| Year | 2018 |
| Volume | 23 |
| Issue | 1 |
| Publication date | 2018-06-19 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Revista Electrónica Educare (JOURNAL) |
| Journal identifiers | ISSN: 1409-4258 • E-ISSN: 1409-4258 |
| Publisher | Universidad Nacional de Costa Rica (PUBLISHER) |
| DOI | 10.15359/ree.23-1.5 |
| OpenAlex | W2903932087 |
| Language | EN |
| Citations received | 5 |
| References cited | 1 |
The phenomenon of dropout, by its complexity and educational and social impact, has been extensively studied to understand the specific causes. In this line of research, the purpose of this study was to analyze explanatory and predictive models of student dropout from university studies at the Instituto Tecnológico de Costa Rica (TEC), based on many variables recorded in the institutional system indicators. The first stage of the analysis considered multinomial regression models to identify the influence of these variables on the dropout. In the second analysis, six machine learning algorithms were evaluated in order to find a model that would predict student dropout. Data analysis showed that the probability of dropping out is related to sociodemographic variables, study program, academic history, scholarship and other benefits, and performance after first semester. In addition, the best predictor of dropout algorithm was the "random forest", a probability of 0.83 to predict the dropout correctly and to capture 34% of the actual student dropout. These results are the first step toward building a more robust predictive model of dropout, which will contribute to preventive decision making in this university
Dropout (neural networks · Econometrics · Economics · Machine learning · Multinomial distribution · Multinomial logistic regression · Random forest · Scholarship · Statistics · Business, Innovation, and Economy · Computer Science · Educational Outcomes and Influences · Educational Research and Science Teaching · Mathematics · Psychology
| Unique citing works | 5 |
|---|---|
| Citations per year | 0,83 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 5 |