墨西哥高中辍学风险预测模型

IF 0.4 Q4 MULTIDISCIPLINARY SCIENCES
Luis Fernando Hernández-Jácquez, Frine Virginia Montes-Ramos
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引用次数: 3

摘要

墨西哥全国高中辍学率在14.5%至16.5%之间波动,实证研究表明,辍学主要与失败有关,而这反过来又与缺乏学习自我调节和学习习惯等问题有关。这项研究的目的是建立一个预测墨西哥高中生辍学风险的模型。开展了一项定量、非实验和横断面研究。自变量是辍学风险,通过辍学问卷进行评估,而预测变量学习习惯、自我调节学习和学习风格(根据参与机构的要求)通过学习习惯问卷、学习策略和动机问卷(CEAM II)进行评估,以及Honey–Alonso学习风格问卷(CHAEA)。为了确定预测方程,使用“Wald后向消除步骤”方法,使用二元逻辑回归模型,以192名农业技术学士学位第一学期学生为样本,他们的年龄在14至16岁之间。一个模型,包括与学习习惯相关的笔记学习计划策略的维度;获得了与自我调节相关的学习自我效能感。这个模型解释了37.0%的现象。结论是,建立辍学风险预测机制可以改善或增加上述维度的发展,从而在一定程度上降低辍学风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelo predictivo del riesgo de abandono escolar en educación media superior en México
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.
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来源期刊
CienciaUat
CienciaUat MULTIDISCIPLINARY SCIENCES-
自引率
33.30%
发文量
24
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