Predictive model of high school students’ dropout risk in Mexico
National high school dropout rates in Mexico, fluctuate between 14.5 % and 16.5 %, and empirical research suggests that dropout is mostly associated with failure, and that this in turn, is related to issues such as lack of learning self-regulation and study habits. The objective of this research was...
Saved in:
| Main Authors: | , |
|---|---|
| 格式: | Online |
| 語言: | spa |
| 出版: |
Universidad Autónoma de Tamaulipas
2020
|
| 在線閱讀: | https://revistaciencia.uat.edu.mx/index.php/CienciaUAT/article/view/1349 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | National high school dropout rates in Mexico, fluctuate between 14.5 % and 16.5 %, and empirical research suggests that dropout is mostly associated with failure, and that this in turn, is related to issues such as lack of learning self-regulation and study habits. The objective of this research was to establish a model that predicts the risk of high school students’ drop in Mexico. A quantitative, non-experimental and cross-sectional research was developed. The independent variable, which was the risk of dropping out of school, was assessed through the School Dropout Questionnaire, while the predictive variables study habits, self-regulation learning and learning styles (as requested by the participating institution) were assessed through the Study Habits Questionnaire, the Learning Strategies and Motivation Questionnaire (CEAM II), and the Honey – Alonso Learning Styles Questionnaire (CHAEA). To determine the predictive equation, the binary logistic regression model was used using the “Wald backward elimination steps” method, with a sample of 192 first semester students of an agricultural technological baccalaureate, whose ages ranged between 14 and 16 years. A model that includes the dimensions of note taking study planning strategies related to study habits; and self-efficacy for learning, related to self-regulation was obtained. This model explained 37.0 % of the phenomenon. It is concluded the establishment of dropout risk prediction mechanisms could be improve or increase the development of the aforementioned dimensions in order to reduce to a certain extent the risk of dropping out. |
|---|
